Data-driven Job Search Engine Using Skills andCompany Attribute Filters

Rohit MuthyalaNew York University

New York NYEmail rrm404nyuedu

Sam WoodStanford University

Stanford CAEmail swood95stanfordedu

Yi Jin Yixing Qin Hua Gao Amit RaiEverString Technology

San Mateo CAEmail data-scienceeverstringcom

AbstractmdashAccording to a report online [34] more than 200million unique users search for jobs online every month Thisincredibly large and fast growing demand has enticed softwaregiants such as Google and Facebook to enter this space which waspreviously dominated by companies such as LinkedIn IndeedDice and CareerBuilder Recently Google released their ldquoAI-powered Jobs Search Enginerdquo ldquoGoogle For Jobsrdquo [30] whileFacebook released ldquoFacebook Jobsrdquo within their platform [32]These current job search engines and platforms allow users tosearch for jobs based on general narrow filters such as job titledate posted experience level company and salary Howeverthey have severely limited filters relating to skill sets such asC++ Python and Java and company related attributes suchas employee size revenue technographics and micro-industriesThese specialized filters can help applicants and companiesconnect at a very personalized relevant and deeper level

In this paper we present a framework that provides anend-to-end ldquoData-driven Jobs Search Enginerdquo This consistsof comprehensive search filters including user skill set-focusedattributes and various company attributes In addition users canalso receive potential contacts of recruiters and senior positionsfor connection and networking opportunities

The high level implementation of the framework is describedas follows1) Collect job postings data in the United States2) Extract meaningful tokens from the postings data using ETL(Extract Transform and Load) pipelines3) Normalize the data set to link company names to their specificcompany websites4) Extract and ranking the skill sets5) Link the company names and websites to their respectivecompany level attributes with the EVERSTRING Company API6) Run user-specific search queries on the database to identifyrelevant job postings and7) Rank the job search results

This framework offers a highly customizable and highlytargeted search experience for end users This framework alsoenables deeper analytics on the job market as a whole byproviding many advanced segmenting dimensions vis-a-vis theskills-based semantic data and company level attributes

Index TermsmdashAdvanced Job Search Data-driven RelevantKeywords Skills Insights Machine Learning Powered Skill SetExtraction Search Results Ranking Job Search Process JobHiring Signals Business Data Processing Big Data

I INTRODUCTION

Does a company utilize certain types of skills more thanothers Does an individual need to have one set of skills for

Fortune 500 companies and another set of skills for small-sized firms What type of skills are required for an individualto get into a particular company or an industry How does anindividual possessing certain skills and preferences for specificindustries and technologies search for jobs Understanding theskill sets required for a job is of importance to professionalsas it governs their employability as well as their decisionsto seek professional certifications This study evaluates theskills required for all types of jobs by analyzing a broad setof online job descriptions gathered from company websitesand from Indeedrsquos API From the descriptions we cleanextract and rank the various skills required for a particularjob using unsupervised algorithmic approaches By combiningjob posting data with EverStringrsquos proprietary database ofcompany attributes the search interface offers much moreflexibility to cater to the userrsquos personal preferences Forexample we are able to support the following real world usecase ldquoFind job opportunities at companies with fewer than 200employees whose revenue is more than 2 Million USD withinthe rsquodigital marketingrsquo micro industry requiring experience inScala or Python and knowledge of Marketo JQuery and CRMsoftwarerdquo As a finishing touch we are also able to provide thecontact information for the relevant recruiters and managers atthe respective companies

There are many companies like Indeed Monster LinkedInWayUp DirectEmployers CareerBuilder SimplyHired Glass-door and Facebook which are active players on the job searchmarket Indeed claims to have more than 200 million uniquevisitors every month and Google has recently entered therecruiting space with its new AI powered job search engine -ldquoGoogle for Jobsrdquo which displays listings from most of the jobsearch sites mentioned above These search interfaces how-ever lack the rich skill set and company attribute preferencesthat job seekers need to find the best jobs matched to theirpersonal preferences their capabilities and their interests andaspirations

The framework in this paper leverages text mining andInformation Retrieval techniques to transform unstructured textinto useful structured data from which interesting relationshipscan be extracted From job postings we extract and rankskills by relevance starting with TF-IDF weighting and thenadjusting the results using topical information based on the

arX

iv1

709

0505

0v1

[cs

IR

] 1

5 Se

p 20

17

job title of the posting Encouraging results are presented afterapplying these techniques to a corpus of job postings extractedfrom Indeed and company websites

In addition to providing more nuanced search capabilitiesthe framework links features and attributes between the se-mantic data contained within the job postings to those ofthe company entities themselves allowing aggregations andanalytics to answer interesting questions on job markets asa whole For instance we can compare the top skill setrequirements between small and large-sized firms (eg basedon revenue number of employees and total funding raised)within a particular industry or subsector or we can identifywhich subsectors are most aggressively hiring for certainpositions or skill sets

This work introduces a methodology to extract and rankskills from job postings and demonstrates the usefulnessof linking advanced company attributes related to each jobposting Leveraging Everstringsrsquos API to provide additionalcontextual features at the company level enhances the userexperience and search capability to identify targeted and bestfit job opportunities across a wider array of dimensions thanis traditionally offered

II RELATED WORK

Two widely popular classes of keyword extraction tech-niques were considered for extractionranking of skill sets inthe job postings One class of keyword extractionranking tech-nique is based on keyword matching or Vector Space modelswith basic TF-IDF weighting [1] The TF-IDF weighting isobtained by using only the content of the document itselfThen several similarity measurements were used to comparethe similarity of the two documents based on their featurevectors [2] The other class of keyword extractionrankingtechnique is based on using context information to improvekeyword extraction Recently there has been lot of work ondeveloping different machine learning methods to make use ofthe context in the document [3] [28] Zhang et al [4] discussesthe use of support vector machines for keyword extractionfrom documents using both the local and global context Thereare number of techniques developed to use local and globalcontext in keyword extraction [3] [4] [5]

We also considered techniques used to enhance informationretrieval using concepts of semantic analysis such as ontologybased similarity measures [9] [10] In these approaches theontology information is used to find similarity between wordsand find words even if the exact match is not availableOther ways in which semantic information is extracted isusing Wordnet libraries Wordnet based approaches have usedconcepts such as relatedness of words for information retrieval[11]-[14] [27] Demonstrated that informative structured snip-pets of the job postings can be generated in an unsupervisedway to improve the user experience for a job search engineCroft et al in his book [15] describes the various uses ofsearch engines in information retrieval Recent works [16]have shown the use of encyclopedic knowledge for informationretrieval Lian et al [17] describe the use of Google distance to

find concept similarity Google distance based approaches havebeen used in various applications such as relevant informationextraction [21] [18] keyword prediction [19] and tag filtering[20] Given the keywords that we need to look for and rankthem based on the documents given is also a similar problem

After an extensive search of the literature we found limitedreferences on skill set-based approaches to help job seekersto search for the best fit jobs Most of the job hiring websitestoday rank the job postings based on the keywords given bythe user The ranking is done considering the entire corpus ofdocuments and the key words given which limits the usersto a single level search [24] has done very interesting workusing the skill set where a user can search for the jobs basedon the skills but this search is still single level where the jobpostings are ranked using the keywords given by the user

Literature on fostering college graduates to make them moreemployable is well grounded and has a rich history in manyfields of study Birch Allen McDonald and Tomaszczyk(2010) studied the considerable mismatch between studentjob expectations and experiences with what the businesscommunity expects [25] discusses the need for accurate skillsassessments of employees in large global client-facing enter-prises and shortcomings of existing systems for obtaining andmanaging expertise Gault Leachand Duey (2010) examinedthe differences between how much training students believethey need versus what employers already expect from the stu-dents when hired These sources definitely note a mismatch inexpectations and skills Tanyel Mitchelland McAlum (1999)in surveying prospective employers and university faculty ontheir perceptions of the skills and abilities business schoolgraduates should possess in to compete in industry foundthat about half of the skills listed by each group differed Inother words what faculty thought were the important skillsversus what prospective employers actually sought did notmatch To identify the mismatch between the skills expectedby employers and those possessed by job seekers [26] profilejob titles by effectively quantifying the relevance of skills

Here we propose a method to get this search to a multi levelwhere a user can search for jobs based on different categorieslike technologies skills revenue company size industry andmany more and the search results are ranked taking a lot offeatures into consideration such as Alexa ranking (measuresthe web traffic for websites ie popularity of the company)social followers revenue user given keywords (skills industrytechnologies) and many more

III DESIGN AND IMPLEMENTATION

At a high level our system deals with text associated withjob postings online to infer and rank the skill set for each jobposting using the job descriptions and the job title The skill setextracted for each job posting is joined with the Everstringrsquosknowledge base to enhance it with the company features Thecombined data is used to search for the jobs with a veryspecific search queries given by the userjob seeker The searchresults are finally ranked using relevance score will describein III-F Some interesting analytics which are useful in the

JobSearchEngine

Data Sources

Machine Learning

Applications

Ranking Search ResultsCompute Engine

Advanced Job Search App Engine

Getting Relevant ContactsApp Engine

Crawled Datajob Postings

Professional social network DataSkill Set

Indeed DataJob Postings

Analytics

Spark MLlibData Processing

ETLDataflow

Transform Data

Normalized DataHDFS Storage

Combined Sources

Skill Set Ranking Machine Learning

Design Diagram Job Search Relevance

Domain Name GenerationMachine Learning

Everstring DataCompany Features

Everstring DataContact Features

Fig 1 Design Diagram - Job Search Engine

job hiring market are developed and shown in this paper Thedatabase generated can be used to find the job postings whichexactly match the usersrsquo requirement and also give the userswith the contacts of the relevant people currently working inthe companies of the corresponding job postingsThe process is described below

1) Extract the job postings and skills data from indeedcompany job pages and professional social networks[23]

2) Extract useful tokens of data and store them in dis-tributed database - ETL(Extract Transform and Load)

3) Normalize clean the data as described in III-B Obtainthe domain name from company name as described inIII-C

4) Extract and rank the skill set required for a job asdescribed in III-D and III-E

5) Merge with the Everstring Data using API to extractuseful Insights

6) Run user specific search queries on the database toreceive relevant job postings in a ranked order IV-A

7) Perform analytics IV-B

A Data Extraction

We constructed a web crawler to crawl the job pages of com-panies to extract the company names urls job descriptionsjob titles and company address This was done by exploitingregularities in the html structure of the companies websiteand by constructing extracting functions to automatically ex-tract company names and descriptions of jobs from arbitrarywebsites Additionally we used Indeedrsquos API to fetch the jobpostings with all the necessary features Here we didnrsquot crawlthe entire job posting web page instead we crawl only theuseful information that is required such as job descriptiontitles company name and address This is in contrary with thetraditional web crawlers which crawl the entire website andlater extract the useful features from the entire unstructuredwebsite data This crawl was done by using an XML based

approach and viewing the task of data extraction as a multi-step process where the goal extends far beyond simpleldquoscreenscrapingrdquo as mentioned in [7] Navigation rules and extractionrules were optimized by hand to produce semi structured dataThe skill sets mentioned in the professional social networksare extracted by leveraging the Linking Open Data projectDBpedia [23] to generate a set of 750k unique skills

B Data Preprocessing

1) Skills Data By generating the co-occurrence countsof the skills [23] we can filter the skills based on a countthreshold which is decided based on the frequency and thenumber of words in the skill We then generate lower casedskills and apply basic normalization techniques on the skillsusing several common patterns For example replacing ldquoamprdquoor ldquo+rdquo with ldquoandrdquo unless ldquorampdrdquo removing the dash in ldquoe-mailrdquo and running basic lemmatization on the skills Whilenormalizing we keep the original lower cased skills and thenormalized skills in a sequence For example if the input isldquoe-mailrdquo we generate a sequence of the original skill and thenormalized skill - Seq(ldquoe-mailrdquoldquoemailrdquo) We then generate alemmatized skill (standard word in WordNet) using the lowercased skill To generate the lemmatized skill we parse the skillinto its part of speech(POS) to obtain the word Ngrams of theskill We then replace any Ngram with the one that is presentin WordNet If the Ngram is not present in WordNet the Ngramis not replaced Using the lemmatized Ngrams we combinethe Ngrams back to generate the lemmatized skill LeveragingEverstringrsquos Human Intelligence Tasks (HITs) system an in-house on-demand workforce platform we filter the 1000 mostfrequently occurred lemmatized skills For example wordslike ldquoteamrdquo ldquoknowledgerdquoldquoinformationrdquo which occurred morefrequently in the dataset and are removed from the skill set asthey act as outliers in our skill set

We construct a lemma dictionary using the lemmatizedskills and its corresponding sequence of normalized andlower cased skills The lemma dictionary is a map from thenormalized and original skill to the lemmatized skill Thenormalized and original skill will be the key in the mapThe sequence key is split to generate two different keys Forexample Seq(ldquoArdquoldquoBrdquo) map to ldquoCrdquo becomes a map fromldquoArdquo rarr ldquoCrdquoand ldquoBrdquo rarr ldquoCrdquo This generates a dictionarythat maps the original and normalized skills to the lemma-tized skills The total number of distinct lemmatized skills isreduced from 750k to 73k after filtering and lemmatization ofskillsFor Example ldquocnetrdquo ldquoc netrdquoldquoc amp netrdquo and ldquocand netrdquo are mapped to ldquoc and netrdquo ldquosystems installa-tionsrdquoldquosystem installationsrdquoldquosystems installationrdquo and ldquosys-tem installingrdquo are mapped to ldquosystem installationrdquo

2) Job Postings Data In this section we discuss multipleinformation extraction (IE) steps performed on the job postingsdata All IE steps are performed on the raw text of the jobtitles company names and job descriptions to retrieve usefuland structured information and to generate and rank the skillsets to take the job search process to a much finer level

bull Title Normalization and Parsing The title string in itsnatural language form is first normalized and then parsedto obtain the management level and the department(s) itfalls under The title normalization steps are1 Replace specific characters such as spacing out com-mas and handling special characters2 Remove level information eg strip out roman numeraltails ldquolevel 1rdquo etc Apply Ngram substitutions egV P rarr V icePresident eminusmailrarr email3 Add acronym forms with periods after each charactereg ceorarr ceoAt a high level we apply character level(1) sequencelevel(2) and token level(3) transformations to normalizethe title In between these steps we use regex to split thetitle at different levels to identify the title NgramtokensWe parse the normalized title to obtain its managementlevel and department(s) The title is classified into oneof five different management levels (C-Level VP-LevelDirector Manager Non-Manager) and one or more de-partments (ldquoAdministrativerdquo ldquoComputing amp ITrdquo ldquoEngi-neeringrdquo ldquoEducatorrdquo ldquoFinancerdquo ldquoHRrdquo ldquoMarketingrdquo andmany others) This parsing is performed using a manuallygenerating mapping from title Ngrams to managementlevels and departments

bull Company Name Normalizer The company name stringin its natural language form is normalized to a form thatfacilitates matching to a website1 Convert the company name to lowercase and replaceldquorsquosrdquo with ldquosrdquo eg ldquoMacyprimesrdquorarr ldquomacysrdquo and convertnon-alphanumeric characters to spaces2 Generate a set of suffixes and prefixes that are com-monly used in company names like ldquollcrdquo ldquoltdrdquo ldquocorprdquoldquoincrdquo ldquocordquo and many others and use this set to drop theoccurrences of these words in the company name3 Generate a set of stop words that are not useful torecognize a company such as ldquotechnologiesrdquo ldquomanage-mentrdquo ldquoservicerdquo ldquopvtrdquo ldquogrouprdquo ldquosolutionsrdquo and manyothers Use this set to drop the occurrences of these wordsin the company names irrespective of their position Thismethod is inspired by the CompanyDepot [8] system

bull Job Description Cleaning To normalize the job descrip-tions we use the same normalization schema as the oneused to normalize the skills data

bull BOW Generation Using Skill Sets The lemma dictionarywhich maps the lemma to the original and normalizedskill is constructed using the skills mentioned in profes-sional social networks We use this lemma dictionary tocount the occurrences of the normalized or the originalskill in the job descriptions and generate a map fromthe lemma skill to its count keeping the track of theoriginal form of the skill Here we create a map fromthe lemmatized skill to the struct of the total lemma countand the map of original or normalized skill Ngram andits count

C Company Name to Website

In order to utilize company firmographic APIs that areavailable on the web we had to convert the normalized com-pany name associated with each job posting to their respectivecompany website To perform this conversion we leveragedEverStringrsquos Company Name to Website service which takesin inputs parameter such as name location (parsed or unparsedstreet city state zip) and outputs the website associatedwith the input with a confidence score EverStringrsquos CompanyName to Website Service was generated by triangulating bothcrawled results as well as multiple purchased data sources togenerate a comprehensive alias table for fast query Using thisservice we were able to convert company names like ldquoAmazonDriverdquo ldquoAmazon Web Servicesrdquo ldquoAmazon Primerdquo ldquoAmazon-Freshrdquo ldquoAmazon HVHrdquoldquoAmazon Corporate LLCrdquo ldquoAmazonLogisticsrdquoldquoAmazon Web Services Incrdquo ldquoAmazoncomdedcLLCrdquoldquoAmazon FulfillmentrdquoldquoAmazon Fulfillment servicesrdquoldquoAmazonrdquo to the companyrsquos website amazoncom with highconfidence scores

D TF-IDF Generation

Document Frequency Generation We begin by countingdocument (here a document refers to a job posting) frequenciesfor all unique skills (lemmas original and normalized skills)present in the skills lemma dictionary computed in III-B1We compute document frequencies for skill lemmas andfilter out lemmas that do not satisfy a minimum documentfrequency threshold For the remaining valid lemmas we addthe original Ngram forms (if different) to the list of documentfrequency counts so we have a unified dictionary whichmaps all the skills in the documents to its count Documentswith zero document length (sum of all the counts of theskills - docLen) are removed from the corpus Reservedkeyword ldquonDocsrdquo is set to represent the number of validdocuments in our corpus which helps in computing theinverse-document-frequency laterLTU term weighting scheme(TF-IDF) The term frequenciesare generated for each document job posting by countingthe number of occurrences of the lemma skill as described inthe ldquoBOW generationrdquo section above The weighting for eachskill in a document is generated using the following formula

LTU =(log2 (tf) + 1) log2

(nDocs

df

)(08 + 02 docLen

avgDocLen )

where tf (Term Frequency) is the number of occurrences ofthis term (the skill for which we are computing the weight) inthe current document docLen(Document Length) is total count(with repetitions) of terms present in the current documentnDocs (Number Documents) is the total number of documents(job postings) in the database avgDocLen (Average DocumentLength) is the average number (with repetitions) of termspresent across all documents df (Document Frequency) is thenumber of documents containing this term LTU weightinguses a pivoted document length normalization scheme basedon the intuition that words that appear the same number

of times in shorter documents should be considered morerelevant than in longer documents This weighting schemaalso incorporates sub-linear term frequency scaling Finallythe scaled TF-IDF is generated for each document by scalingthe maximum TF-IDF value of a skill to 1

E Weights Generation

Ranking the skills using just the TF-IDF score did notproduce good results as we have only taken the job descrip-tions into consideration for generating this score While thejob description contains an extensive amount of informationabout the company the benefits they provide is mixed Theprimary concern is much of the information acts as a noisein our system This drives us to include one more additionaldimensions for a better ranking In order to achieve this weuse the job title to generate a weighting schema on top ofthe TF-IDF that we have computed previously Intuitively wewant a skill to be weighted higher if it occurs more frequentlyunder a title Ngram If a particular skill is very common inthe dataset then it should be weighted lower For example jobpostings with ldquoSoftwarerdquo in the title will weigh the skills likeldquoCrdquoldquoC++rdquoldquoJavardquoldquoOOPrdquo higher as they frequently occur inthe job posting with ldquoSoftwarerdquo in their title They will notoccur in many non technical job postings (a high percentageof job postings in our corpus) which boosts the probabilityof having these skills given ldquoSoftwarerdquo in the title In thisway we have added one more dimension to weigh the skillsby considering the titles of the jobs The weighting schema ismathematically explained using a formula which is explainedin the following sections

1) Title Ngram Generation Initially we normalize the titleand remove all punctuations present in the title We then splitthe title to generate a set of all unigrams present in the titlesThis set is filtered based on the occurrence frequency and stopwords We then generate bigrams from the title by looking atthe sequence of two words in the original normalized title andfiltering out the bigrams by checking if it is present in theoriginal normalized title Eg If we have ldquoBig Data SoftwareEngineerrdquo as a title we look for all bigram sequences (ldquoBigDatardquoldquoData Softwarerdquo and ldquoSoftware Engineerrdquo) in the titleand add the bigrams that are present in the title to generate thefinal list of title Ngrams In this example ldquoSoftware Engineerrdquowill be added to the title Ngrams list After generating this listwe split the normalized title to obtain the unigrams bigramsand filter out the grams that are not present in the title toobtain the title Ngram list for each job title

2) Weight Matrix Generation Now that we have an arrayof title Ngrams for every title in the corpus we use thisinformation to rank the skills that are extracted for eachjob posting (ldquo BOW Generationrdquo in the section above) bygenerating a weight for every skill present in a job postingusing a count matrix between the skills and the title Ngramsacross all the documents The count matrix [22] is computedby generating the term frequency of a particular skill presentin all job descriptions that has a particular title Ngram in itstitle Eg we obtain a count array (of length equal to number

of unique skills) for every unique title Ngram This generatesa matrix giving the count of the occurrences of all skillscorresponding to a particular title Ngram Using this countmatrix we compute the weight for every skill in a job postingby adopting the following formula

weight(skill) =prob(skill|titleNgram)

prob(skill)

The above weighting formula supports our intuition that theskill which is more common across all the documents willbe weighted lower and the skill is weighted higher if theprobability of finding it in all the job descriptions which has aparticular title Ngram is higher For a particular skill in a jobposting we obtain a different weight for each title Ngram ofa job posting because each title in a job posting has multipletitle Ngrams To generate a single weight for each skill in ajob posting we average all of the weights computed for thisskill and all of the job title Ngrams

3) Final Score Generation In the previous section wecomputed the weights for all of the skills in a job postingThis weight is multiplied with the TF-IDFLTU computedpreviously to obtain a score for a particular skill in a jobposting This score and many other external factors about thecompany are used to rank the skills corresponding to a jobposting

F Query Results Ranking

After processing the query the search results are rankedbased on the following formula

Rank(document) = AttributeScore lowastAvg(weight(skill))

Avg(weight(skill)) is the average of all the weights III-E of theskills corresponding to a job posting As we have a weightfor all the skills for each job posting we take the skills givenby the user and compute the aggregated score which is theaverage of user given skills weight

AttributeScore = feedback lowast af lowast ef lowast nlf lowast cks

where af is the alexa Factor [33] ef is the Employee Factor(Afactor computed using the information of the number ofemployees in the company) nlf is the Number of LemmasFactor cks = 2

radicscore where score is the micro industry

keyword score and feedback is the score defined by the numberof user clicks

IV RESULTS

In this section we will show an example of how a jobseeker can search for relevant jobs based on the personrsquosskill set the desired industry space the preferred technologystack and many other skill and company attributes The jobseeker can then query for relevant contacts within the companywhich helps the job seeker to connect and network We thencompared our search results with other job search websitessuch as Indeed and Google

In addition this section presents examples of enhancedanalytics including analysis of the most common skill sets

required by ldquostaffing servicesrdquoldquohealth servicesrdquo and ldquorestau-rant chainrdquo industries the most desired technologies in the USjob market and presenting the top recruiting companies thatare looking for prospects with specific skill sets (ldquoJavardquo andldquoMasters Degreerdquo) or uses specific technologies (ldquoTableaurdquoand ldquoMongoDBrdquo)

A Job Search Results

Query-1 A User with a bachelorrsquos degree and has Pythonand Scala programming skills wants to search for the jobs incompanies which uses jQuery technology wants to work inthe ldquoengineeringrdquo vertical with companies whose revenue isgreat than 1 Million USD (the revenue that we show in thefollowing results are in terms of 1000 USD ) and the numberof employees to be between 50 and 200

The top 40 companies that we identified with the abovesearch query are shown in Fig 2

Fig 2 Top 40 Companies Found Using Query-1

Showing detailed job results for the top three companiesfrom Fig 2 in ranked order is shown in Fig 3 To clarifyon few results which may look odd we have results likeldquoSenior Full stack Java Developerrdquo shown up for the abovequery When looked into the job description [35] (Job Keyd93199bf4c06f3b4) we have the following statement ldquoStrongprofessional experience with Java and at least one otherlanguage - Python Go Scala etcrdquo So this supports our searchquery Here we can also show the ranked job postings basedon the query but here we want to present the results based onthe company similar to topic search engine

The contact information of the recruiters of the top 40companies in the Fig 2 are shown in Fig 4 Consideringthe privacy violation we are not showing the phone numbersand the emails of the recruiters in this paper

No job search website supports querying with number ofemployees and revenue When we searched for a combinationof the five keywords (ldquojQueryrdquo ldquoengineeringrdquo ldquoPythonrdquoldquoScalardquo ldquobachelor degreerdquo) described above we received

Fig 3 Job Details for Top Three Companies

zero results using Googlersquos job search Indeedrsquos job searchproduced many results satisfying any one of the keywords iejobs that satisfy either ldquojQueryrdquo or ldquoengineeringrdquo or ldquoPythonrdquoor ldquoScalardquo or ldquobachelor degreerdquo Our results are not exhaustiveas we ran this experiment on a limited set of job postingsHowever when this approach is used on a larger dataset weobtain much better results and with a better user experience

B Analytics

Analytic-1 The top 40 technologies across all job postingsare shown in Fig 5a The top 40 skills that companies in therestaurant chain industry are looking for are shown in Fig5b The top 40 skills that companies in the staffing servicesindustry are looking for are shown in Fig 5c Top 40 skillsthat companies in the health services industry are looking forare shown in Fig 5d

Analytic-2 Top companies that use ldquoTableaurdquo and ldquoMon-goDBrdquo technologies are shown in Fig 6a and top companieswhich hire people with ldquomaster degreerdquo and have ldquoJavardquoprogramming skills are shown in Fig 6b

V CONCLUSIONS AND FUTURE WORK

In this paper we have discussed a methodology that en-hances the job searching experience of users by adding skillset and company attribute filters Job seekers can pinpointjobs by filtering on relevant skill sets technologies numberof employees micro industries and many other attributes Thefiltered jobs are ranked using a relevance score derived froma weighted combination of skill sets and external factors ofcompanies as described in III-F We have also developedadvanced analytics on the data as shown in IV-B

We can additionally enhance the analytics with a temporalaspect by analyzing collected jobs information over time andidentifying trends in the skill set requirements for differentindustries We can also add more filters such as additionalsocial attributes that identify companiesrsquo social presence on

Fig 4 Top 40 Companies with Contacts

(a) Top 40 Technologies(b) Top 40 Skills in theRestaurant Chain Industry

(c) Top 40 Skills in theStaffing Services Industry

(d) Top 40 Skills in theHealth Services Industry

Fig 5 Analytic -1

(a) Top companies that usesldquoTableaurdquo and ldquoMongoDBrdquo

(b) Top recruiters that requirea person who knows ldquojavardquoand has a ldquomaster degreerdquo

Fig 6 Analytic - 2

the web We can also add higher level attributes that arederived from a combination of existing attributes For examplea higher level attribute such as ldquoMarketing Sophisticationrdquo canbe derived by a combination of social presence and advertisingtechnologies attributes

ACKNOWLEDGMENT

We would like to thank Everstring for generously providingthe datasets needed for our research and assisting us throughout this project and being a source of motivation and knowl-edge We would also like to thank Everstringrsquos HPC (HighPerformance Computing) Group for providing us with constanttechnical support over their Hadoop cluster

REFERENCES

[1] Joachims and Thorsten A Probabilistic Analysis of the Rocchio Al-gorithm with TF-IDF for Text Categorization No CMU-CS-96-118Carnegie-Mellon Univ Pittsburgh Pa Dept of Computer Science 1996

[2] W T Yih and C Meek ldquoImproving similarity measures for short seg-ments of textrdquo in Proceedings of the National Conference on ArtificialIntelligence vol 22 no 2 Menlo Park CA Cambridge MA LondonAAAI Press MIT Press 1999 2007

[3] K Jasmeen and V Gupta ldquoEffective approaches for extraction ofkeywordsrdquo IJCSI International Journal of Computer Science Issues 762010

[4] K Zhang et al ldquoKeyword extraction using support vector machinerdquoAdvances in Web-Age Information Management pp 85-96 2006

[5] H Bao and D Zhen ldquoAn Extended Keyword Extraction MethodrdquoPhysics Procedia vol 24 pp 1120-1127 2012

[6] Y Yorozu M Hirano K Oka and Y Tagawa ldquoElectron spectroscopystudies on magneto-optical media and plastic substrate interfacerdquo IEEETransl J Magn Japan vol 2 pp 740ndash741 August 1987 [Digests 9thAnnual Conf Magnetics Japan p 301 1982]

[7] Jussi Myllymaki ldquoEffective Web Data Extraction with Standard XMLTechnologiesrdquo

[8] Qiaoling Liu Faizan Javed and Matt Mcnair CompanyDepotldquordquo Em-ployer Name Normalization in the Online Recruitment Industryrdquo

[9] Fernndez and Miriam et al ldquoSemantically enhanced Information Re-trieval an ontology-based approachrdquo Web semantics Science servicesand agents on the worldwide web 94 pp 434-452 2011

[10] K Soner et al ldquoAn ontology-based retrieval system using semanticindexingrdquo Information Systems vol 37 no 4 pp 294-305 2012

[11] J J Feng et al ldquoKeyword extraction based on sequential pattern min-ingrdquo in Proceedings of the Third International Conference on InternetMultimedia Computing and Service ACM 2011

[12] F Liu F F Liu and Y Liu ldquoA supervised framework for keywordextraction from meeting transcriptsrdquo Audio Speech and LanguageProcessing IEEE Transactions on vol19 no 3 pp 538-548 2011

[13] S H Xu S H Yang and F CM Lau ldquoKeyword extraction and headlinegeneration using novel word featuresrdquo in Proc of the Twenty-FourthAAAI Conference on Artificial Intelligence 2010

[14] I Keisuke and N Mc Cracken ldquoAutomated Keyword Extraction ofLearning Materials Using Semantic Relationsrdquo 2010

[15] C W Bruce D Metzler and T Strohman Search engines Informationretrieval in practice Addison-Wesley 2010

[16] V P Schaik and J Ling ldquoAn integrated model of interaction experiencefor information retrieval in a Web-based encyclopaediardquo Interacting withComputers vol 23 no1 PP18-32 2011

[17] L Yu et al ldquoConcepts Similarity Algorithm Based on Google and KLDistancerdquo Jisuanji Gongcheng Computer Engineering vol 37 no 192011

[18] P I Chen S J Lin and Y C Chu ldquoUsing Google latent semanticdistance to extract the most relevant informationrdquo Expert Systems withApplications vol 38 no 6 pp 7349-7358 2011

[19] P I Chen and S J Lin ldquoAutomatic keyword prediction using Googlesimilarity distancerdquo Expert Systems with Applications vol 37 no3pp 1928-1938 2010

[20] X M Qian X H Hua and X S Hou ldquoTag filtering based onsimilar compatible principlerdquo Image Processing (ICIP) 2012 19th IEEEInternational Conference on IEEE 2012

[21] P I Chen and S J Lin ldquoWord AdHoc network using Google coredistance to extract the most relevant informationrdquo Knowledge-BasedSystems vol 24 no 3 pp 393-405 2011

[22] Ioannis Antonellis and Efstratios Gallopoulos ldquoExploring term-document matrices from matrix models in text miningrdquo

[23] Emmanuel Malherbe and Marie-Aude Aufaure ldquoBridge the terminologygap between recruiters and candidates A multilingual skills base builtfrom social media and linked datardquo

[24] Simon Hughes and his team ldquohttpwwwdicecomskillsrdquo[25] Kush R VarshneyJun Wang Aleksandra MojsilovicDongping Fang

and John H Bauer ldquoPredicting and Recommending Skills in the SocialEnterpriserdquo

[26] Wenjun ZhouYun ZhuFaizan Javed ldquoQuantifying skill relevance to jobtitlesrdquo

[27] Nikita SpirinKarrie Karahalios ldquoUnsupervised Approach to GenerateInformative Structured Snippets for Job Search Enginesrdquo

[28] Khalifeh AlJadda Mohammed KorayemCamilo Ortiz Chris RussellDavid Bernal Lamar Payson Scott Brown and Trey Grainger ldquoAug-menting Recommendation Systems Using a Model of Semantically-related Terms Extracted from User Behaviorrdquo

[29] indeedcom rsquoIndeed Job Search engine APIrsquo2017 [Online] Availablehttpsmarketmashapecomindeedindeed [Accessed 1- Jun- 2017]

[30] googlecom rsquoGoogle launches its AI-powered jobs search enginersquo2017 [Online] Available httpstechcrunchcom20170620google-launches-its-ai-powered-jobs-search-engine [Accessed 1- Aug- 2017]

[31] everstringcom rsquoEverstring APIrsquo 2017 [Online] Availablehttpsapieverstringcomv1companiesdiscover [Accessed 1- Jun-2017]

[32] facebookcom rsquoFacebook job Search Pagersquo 2017[Online] Availablehttpstechcrunchcom20161107jobbook [Accessed 15- July-2017]

[33] alexacom rsquoFind Website Traffic Statistics and Analyticsrsquo Availablehttpwwwalexacomsiteinfo[Accessed 1- Jun- 2017]

[34] Time Tech rsquoGoogle Wants to Help You Search for a New Jobrsquo2017 [Online] Available httptimecom4824968google-job-search[Accessed 20- Jun- 2017]

[35] Example Job pagehttpswwwindeedcomrcclkjk=d93199bf4c06f3bampfccid=d85b448de778e20c

job title of the posting Encouraging results are presented afterapplying these techniques to a corpus of job postings extractedfrom Indeed and company websites

In addition to providing more nuanced search capabilitiesthe framework links features and attributes between the se-mantic data contained within the job postings to those ofthe company entities themselves allowing aggregations andanalytics to answer interesting questions on job markets asa whole For instance we can compare the top skill setrequirements between small and large-sized firms (eg basedon revenue number of employees and total funding raised)within a particular industry or subsector or we can identifywhich subsectors are most aggressively hiring for certainpositions or skill sets

This work introduces a methodology to extract and rankskills from job postings and demonstrates the usefulnessof linking advanced company attributes related to each jobposting Leveraging Everstringsrsquos API to provide additionalcontextual features at the company level enhances the userexperience and search capability to identify targeted and bestfit job opportunities across a wider array of dimensions thanis traditionally offered

II RELATED WORK

Two widely popular classes of keyword extraction tech-niques were considered for extractionranking of skill sets inthe job postings One class of keyword extractionranking tech-nique is based on keyword matching or Vector Space modelswith basic TF-IDF weighting [1] The TF-IDF weighting isobtained by using only the content of the document itselfThen several similarity measurements were used to comparethe similarity of the two documents based on their featurevectors [2] The other class of keyword extractionrankingtechnique is based on using context information to improvekeyword extraction Recently there has been lot of work ondeveloping different machine learning methods to make use ofthe context in the document [3] [28] Zhang et al [4] discussesthe use of support vector machines for keyword extractionfrom documents using both the local and global context Thereare number of techniques developed to use local and globalcontext in keyword extraction [3] [4] [5]

We also considered techniques used to enhance informationretrieval using concepts of semantic analysis such as ontologybased similarity measures [9] [10] In these approaches theontology information is used to find similarity between wordsand find words even if the exact match is not availableOther ways in which semantic information is extracted isusing Wordnet libraries Wordnet based approaches have usedconcepts such as relatedness of words for information retrieval[11]-[14] [27] Demonstrated that informative structured snip-pets of the job postings can be generated in an unsupervisedway to improve the user experience for a job search engineCroft et al in his book [15] describes the various uses ofsearch engines in information retrieval Recent works [16]have shown the use of encyclopedic knowledge for informationretrieval Lian et al [17] describe the use of Google distance to

find concept similarity Google distance based approaches havebeen used in various applications such as relevant informationextraction [21] [18] keyword prediction [19] and tag filtering[20] Given the keywords that we need to look for and rankthem based on the documents given is also a similar problem

After an extensive search of the literature we found limitedreferences on skill set-based approaches to help job seekersto search for the best fit jobs Most of the job hiring websitestoday rank the job postings based on the keywords given bythe user The ranking is done considering the entire corpus ofdocuments and the key words given which limits the usersto a single level search [24] has done very interesting workusing the skill set where a user can search for the jobs basedon the skills but this search is still single level where the jobpostings are ranked using the keywords given by the user

Literature on fostering college graduates to make them moreemployable is well grounded and has a rich history in manyfields of study Birch Allen McDonald and Tomaszczyk(2010) studied the considerable mismatch between studentjob expectations and experiences with what the businesscommunity expects [25] discusses the need for accurate skillsassessments of employees in large global client-facing enter-prises and shortcomings of existing systems for obtaining andmanaging expertise Gault Leachand Duey (2010) examinedthe differences between how much training students believethey need versus what employers already expect from the stu-dents when hired These sources definitely note a mismatch inexpectations and skills Tanyel Mitchelland McAlum (1999)in surveying prospective employers and university faculty ontheir perceptions of the skills and abilities business schoolgraduates should possess in to compete in industry foundthat about half of the skills listed by each group differed Inother words what faculty thought were the important skillsversus what prospective employers actually sought did notmatch To identify the mismatch between the skills expectedby employers and those possessed by job seekers [26] profilejob titles by effectively quantifying the relevance of skills

Here we propose a method to get this search to a multi levelwhere a user can search for jobs based on different categorieslike technologies skills revenue company size industry andmany more and the search results are ranked taking a lot offeatures into consideration such as Alexa ranking (measuresthe web traffic for websites ie popularity of the company)social followers revenue user given keywords (skills industrytechnologies) and many more

III DESIGN AND IMPLEMENTATION

At a high level our system deals with text associated withjob postings online to infer and rank the skill set for each jobposting using the job descriptions and the job title The skill setextracted for each job posting is joined with the Everstringrsquosknowledge base to enhance it with the company features Thecombined data is used to search for the jobs with a veryspecific search queries given by the userjob seeker The searchresults are finally ranked using relevance score will describein III-F Some interesting analytics which are useful in the

JobSearchEngine

Data Sources

Machine Learning

Applications

Ranking Search ResultsCompute Engine

Advanced Job Search App Engine

Getting Relevant ContactsApp Engine

Crawled Datajob Postings

Professional social network DataSkill Set

Indeed DataJob Postings

Analytics

Spark MLlibData Processing

ETLDataflow

Transform Data

Normalized DataHDFS Storage

Combined Sources

Skill Set Ranking Machine Learning

Design Diagram Job Search Relevance

Domain Name GenerationMachine Learning

Everstring DataCompany Features

Everstring DataContact Features

Fig 1 Design Diagram - Job Search Engine

job hiring market are developed and shown in this paper Thedatabase generated can be used to find the job postings whichexactly match the usersrsquo requirement and also give the userswith the contacts of the relevant people currently working inthe companies of the corresponding job postingsThe process is described below

1) Extract the job postings and skills data from indeedcompany job pages and professional social networks[23]

2) Extract useful tokens of data and store them in dis-tributed database - ETL(Extract Transform and Load)

3) Normalize clean the data as described in III-B Obtainthe domain name from company name as described inIII-C

4) Extract and rank the skill set required for a job asdescribed in III-D and III-E

5) Merge with the Everstring Data using API to extractuseful Insights

6) Run user specific search queries on the database toreceive relevant job postings in a ranked order IV-A

7) Perform analytics IV-B

A Data Extraction

We constructed a web crawler to crawl the job pages of com-panies to extract the company names urls job descriptionsjob titles and company address This was done by exploitingregularities in the html structure of the companies websiteand by constructing extracting functions to automatically ex-tract company names and descriptions of jobs from arbitrarywebsites Additionally we used Indeedrsquos API to fetch the jobpostings with all the necessary features Here we didnrsquot crawlthe entire job posting web page instead we crawl only theuseful information that is required such as job descriptiontitles company name and address This is in contrary with thetraditional web crawlers which crawl the entire website andlater extract the useful features from the entire unstructuredwebsite data This crawl was done by using an XML based

approach and viewing the task of data extraction as a multi-step process where the goal extends far beyond simpleldquoscreenscrapingrdquo as mentioned in [7] Navigation rules and extractionrules were optimized by hand to produce semi structured dataThe skill sets mentioned in the professional social networksare extracted by leveraging the Linking Open Data projectDBpedia [23] to generate a set of 750k unique skills

B Data Preprocessing

1) Skills Data By generating the co-occurrence countsof the skills [23] we can filter the skills based on a countthreshold which is decided based on the frequency and thenumber of words in the skill We then generate lower casedskills and apply basic normalization techniques on the skillsusing several common patterns For example replacing ldquoamprdquoor ldquo+rdquo with ldquoandrdquo unless ldquorampdrdquo removing the dash in ldquoe-mailrdquo and running basic lemmatization on the skills Whilenormalizing we keep the original lower cased skills and thenormalized skills in a sequence For example if the input isldquoe-mailrdquo we generate a sequence of the original skill and thenormalized skill - Seq(ldquoe-mailrdquoldquoemailrdquo) We then generate alemmatized skill (standard word in WordNet) using the lowercased skill To generate the lemmatized skill we parse the skillinto its part of speech(POS) to obtain the word Ngrams of theskill We then replace any Ngram with the one that is presentin WordNet If the Ngram is not present in WordNet the Ngramis not replaced Using the lemmatized Ngrams we combinethe Ngrams back to generate the lemmatized skill LeveragingEverstringrsquos Human Intelligence Tasks (HITs) system an in-house on-demand workforce platform we filter the 1000 mostfrequently occurred lemmatized skills For example wordslike ldquoteamrdquo ldquoknowledgerdquoldquoinformationrdquo which occurred morefrequently in the dataset and are removed from the skill set asthey act as outliers in our skill set

We construct a lemma dictionary using the lemmatizedskills and its corresponding sequence of normalized andlower cased skills The lemma dictionary is a map from thenormalized and original skill to the lemmatized skill Thenormalized and original skill will be the key in the mapThe sequence key is split to generate two different keys Forexample Seq(ldquoArdquoldquoBrdquo) map to ldquoCrdquo becomes a map fromldquoArdquo rarr ldquoCrdquoand ldquoBrdquo rarr ldquoCrdquo This generates a dictionarythat maps the original and normalized skills to the lemma-tized skills The total number of distinct lemmatized skills isreduced from 750k to 73k after filtering and lemmatization ofskillsFor Example ldquocnetrdquo ldquoc netrdquoldquoc amp netrdquo and ldquocand netrdquo are mapped to ldquoc and netrdquo ldquosystems installa-tionsrdquoldquosystem installationsrdquoldquosystems installationrdquo and ldquosys-tem installingrdquo are mapped to ldquosystem installationrdquo

2) Job Postings Data In this section we discuss multipleinformation extraction (IE) steps performed on the job postingsdata All IE steps are performed on the raw text of the jobtitles company names and job descriptions to retrieve usefuland structured information and to generate and rank the skillsets to take the job search process to a much finer level

bull Title Normalization and Parsing The title string in itsnatural language form is first normalized and then parsedto obtain the management level and the department(s) itfalls under The title normalization steps are1 Replace specific characters such as spacing out com-mas and handling special characters2 Remove level information eg strip out roman numeraltails ldquolevel 1rdquo etc Apply Ngram substitutions egV P rarr V icePresident eminusmailrarr email3 Add acronym forms with periods after each charactereg ceorarr ceoAt a high level we apply character level(1) sequencelevel(2) and token level(3) transformations to normalizethe title In between these steps we use regex to split thetitle at different levels to identify the title NgramtokensWe parse the normalized title to obtain its managementlevel and department(s) The title is classified into oneof five different management levels (C-Level VP-LevelDirector Manager Non-Manager) and one or more de-partments (ldquoAdministrativerdquo ldquoComputing amp ITrdquo ldquoEngi-neeringrdquo ldquoEducatorrdquo ldquoFinancerdquo ldquoHRrdquo ldquoMarketingrdquo andmany others) This parsing is performed using a manuallygenerating mapping from title Ngrams to managementlevels and departments

bull Company Name Normalizer The company name stringin its natural language form is normalized to a form thatfacilitates matching to a website1 Convert the company name to lowercase and replaceldquorsquosrdquo with ldquosrdquo eg ldquoMacyprimesrdquorarr ldquomacysrdquo and convertnon-alphanumeric characters to spaces2 Generate a set of suffixes and prefixes that are com-monly used in company names like ldquollcrdquo ldquoltdrdquo ldquocorprdquoldquoincrdquo ldquocordquo and many others and use this set to drop theoccurrences of these words in the company name3 Generate a set of stop words that are not useful torecognize a company such as ldquotechnologiesrdquo ldquomanage-mentrdquo ldquoservicerdquo ldquopvtrdquo ldquogrouprdquo ldquosolutionsrdquo and manyothers Use this set to drop the occurrences of these wordsin the company names irrespective of their position Thismethod is inspired by the CompanyDepot [8] system

bull Job Description Cleaning To normalize the job descrip-tions we use the same normalization schema as the oneused to normalize the skills data

bull BOW Generation Using Skill Sets The lemma dictionarywhich maps the lemma to the original and normalizedskill is constructed using the skills mentioned in profes-sional social networks We use this lemma dictionary tocount the occurrences of the normalized or the originalskill in the job descriptions and generate a map fromthe lemma skill to its count keeping the track of theoriginal form of the skill Here we create a map fromthe lemmatized skill to the struct of the total lemma countand the map of original or normalized skill Ngram andits count

C Company Name to Website

In order to utilize company firmographic APIs that areavailable on the web we had to convert the normalized com-pany name associated with each job posting to their respectivecompany website To perform this conversion we leveragedEverStringrsquos Company Name to Website service which takesin inputs parameter such as name location (parsed or unparsedstreet city state zip) and outputs the website associatedwith the input with a confidence score EverStringrsquos CompanyName to Website Service was generated by triangulating bothcrawled results as well as multiple purchased data sources togenerate a comprehensive alias table for fast query Using thisservice we were able to convert company names like ldquoAmazonDriverdquo ldquoAmazon Web Servicesrdquo ldquoAmazon Primerdquo ldquoAmazon-Freshrdquo ldquoAmazon HVHrdquoldquoAmazon Corporate LLCrdquo ldquoAmazonLogisticsrdquoldquoAmazon Web Services Incrdquo ldquoAmazoncomdedcLLCrdquoldquoAmazon FulfillmentrdquoldquoAmazon Fulfillment servicesrdquoldquoAmazonrdquo to the companyrsquos website amazoncom with highconfidence scores

D TF-IDF Generation

Document Frequency Generation We begin by countingdocument (here a document refers to a job posting) frequenciesfor all unique skills (lemmas original and normalized skills)present in the skills lemma dictionary computed in III-B1We compute document frequencies for skill lemmas andfilter out lemmas that do not satisfy a minimum documentfrequency threshold For the remaining valid lemmas we addthe original Ngram forms (if different) to the list of documentfrequency counts so we have a unified dictionary whichmaps all the skills in the documents to its count Documentswith zero document length (sum of all the counts of theskills - docLen) are removed from the corpus Reservedkeyword ldquonDocsrdquo is set to represent the number of validdocuments in our corpus which helps in computing theinverse-document-frequency laterLTU term weighting scheme(TF-IDF) The term frequenciesare generated for each document job posting by countingthe number of occurrences of the lemma skill as described inthe ldquoBOW generationrdquo section above The weighting for eachskill in a document is generated using the following formula

LTU =(log2 (tf) + 1) log2

(nDocs

df

)(08 + 02 docLen

avgDocLen )

where tf (Term Frequency) is the number of occurrences ofthis term (the skill for which we are computing the weight) inthe current document docLen(Document Length) is total count(with repetitions) of terms present in the current documentnDocs (Number Documents) is the total number of documents(job postings) in the database avgDocLen (Average DocumentLength) is the average number (with repetitions) of termspresent across all documents df (Document Frequency) is thenumber of documents containing this term LTU weightinguses a pivoted document length normalization scheme basedon the intuition that words that appear the same number

of times in shorter documents should be considered morerelevant than in longer documents This weighting schemaalso incorporates sub-linear term frequency scaling Finallythe scaled TF-IDF is generated for each document by scalingthe maximum TF-IDF value of a skill to 1

E Weights Generation

Ranking the skills using just the TF-IDF score did notproduce good results as we have only taken the job descrip-tions into consideration for generating this score While thejob description contains an extensive amount of informationabout the company the benefits they provide is mixed Theprimary concern is much of the information acts as a noisein our system This drives us to include one more additionaldimensions for a better ranking In order to achieve this weuse the job title to generate a weighting schema on top ofthe TF-IDF that we have computed previously Intuitively wewant a skill to be weighted higher if it occurs more frequentlyunder a title Ngram If a particular skill is very common inthe dataset then it should be weighted lower For example jobpostings with ldquoSoftwarerdquo in the title will weigh the skills likeldquoCrdquoldquoC++rdquoldquoJavardquoldquoOOPrdquo higher as they frequently occur inthe job posting with ldquoSoftwarerdquo in their title They will notoccur in many non technical job postings (a high percentageof job postings in our corpus) which boosts the probabilityof having these skills given ldquoSoftwarerdquo in the title In thisway we have added one more dimension to weigh the skillsby considering the titles of the jobs The weighting schema ismathematically explained using a formula which is explainedin the following sections

1) Title Ngram Generation Initially we normalize the titleand remove all punctuations present in the title We then splitthe title to generate a set of all unigrams present in the titlesThis set is filtered based on the occurrence frequency and stopwords We then generate bigrams from the title by looking atthe sequence of two words in the original normalized title andfiltering out the bigrams by checking if it is present in theoriginal normalized title Eg If we have ldquoBig Data SoftwareEngineerrdquo as a title we look for all bigram sequences (ldquoBigDatardquoldquoData Softwarerdquo and ldquoSoftware Engineerrdquo) in the titleand add the bigrams that are present in the title to generate thefinal list of title Ngrams In this example ldquoSoftware Engineerrdquowill be added to the title Ngrams list After generating this listwe split the normalized title to obtain the unigrams bigramsand filter out the grams that are not present in the title toobtain the title Ngram list for each job title

2) Weight Matrix Generation Now that we have an arrayof title Ngrams for every title in the corpus we use thisinformation to rank the skills that are extracted for eachjob posting (ldquo BOW Generationrdquo in the section above) bygenerating a weight for every skill present in a job postingusing a count matrix between the skills and the title Ngramsacross all the documents The count matrix [22] is computedby generating the term frequency of a particular skill presentin all job descriptions that has a particular title Ngram in itstitle Eg we obtain a count array (of length equal to number

of unique skills) for every unique title Ngram This generatesa matrix giving the count of the occurrences of all skillscorresponding to a particular title Ngram Using this countmatrix we compute the weight for every skill in a job postingby adopting the following formula

weight(skill) =prob(skill|titleNgram)

prob(skill)

The above weighting formula supports our intuition that theskill which is more common across all the documents willbe weighted lower and the skill is weighted higher if theprobability of finding it in all the job descriptions which has aparticular title Ngram is higher For a particular skill in a jobposting we obtain a different weight for each title Ngram ofa job posting because each title in a job posting has multipletitle Ngrams To generate a single weight for each skill in ajob posting we average all of the weights computed for thisskill and all of the job title Ngrams

3) Final Score Generation In the previous section wecomputed the weights for all of the skills in a job postingThis weight is multiplied with the TF-IDFLTU computedpreviously to obtain a score for a particular skill in a jobposting This score and many other external factors about thecompany are used to rank the skills corresponding to a jobposting

F Query Results Ranking

After processing the query the search results are rankedbased on the following formula

Rank(document) = AttributeScore lowastAvg(weight(skill))

Avg(weight(skill)) is the average of all the weights III-E of theskills corresponding to a job posting As we have a weightfor all the skills for each job posting we take the skills givenby the user and compute the aggregated score which is theaverage of user given skills weight

AttributeScore = feedback lowast af lowast ef lowast nlf lowast cks

where af is the alexa Factor [33] ef is the Employee Factor(Afactor computed using the information of the number ofemployees in the company) nlf is the Number of LemmasFactor cks = 2

radicscore where score is the micro industry

keyword score and feedback is the score defined by the numberof user clicks

IV RESULTS

In this section we will show an example of how a jobseeker can search for relevant jobs based on the personrsquosskill set the desired industry space the preferred technologystack and many other skill and company attributes The jobseeker can then query for relevant contacts within the companywhich helps the job seeker to connect and network We thencompared our search results with other job search websitessuch as Indeed and Google

In addition this section presents examples of enhancedanalytics including analysis of the most common skill sets

required by ldquostaffing servicesrdquoldquohealth servicesrdquo and ldquorestau-rant chainrdquo industries the most desired technologies in the USjob market and presenting the top recruiting companies thatare looking for prospects with specific skill sets (ldquoJavardquo andldquoMasters Degreerdquo) or uses specific technologies (ldquoTableaurdquoand ldquoMongoDBrdquo)

A Job Search Results

Query-1 A User with a bachelorrsquos degree and has Pythonand Scala programming skills wants to search for the jobs incompanies which uses jQuery technology wants to work inthe ldquoengineeringrdquo vertical with companies whose revenue isgreat than 1 Million USD (the revenue that we show in thefollowing results are in terms of 1000 USD ) and the numberof employees to be between 50 and 200

The top 40 companies that we identified with the abovesearch query are shown in Fig 2

Fig 2 Top 40 Companies Found Using Query-1

Showing detailed job results for the top three companiesfrom Fig 2 in ranked order is shown in Fig 3 To clarifyon few results which may look odd we have results likeldquoSenior Full stack Java Developerrdquo shown up for the abovequery When looked into the job description [35] (Job Keyd93199bf4c06f3b4) we have the following statement ldquoStrongprofessional experience with Java and at least one otherlanguage - Python Go Scala etcrdquo So this supports our searchquery Here we can also show the ranked job postings basedon the query but here we want to present the results based onthe company similar to topic search engine

The contact information of the recruiters of the top 40companies in the Fig 2 are shown in Fig 4 Consideringthe privacy violation we are not showing the phone numbersand the emails of the recruiters in this paper

No job search website supports querying with number ofemployees and revenue When we searched for a combinationof the five keywords (ldquojQueryrdquo ldquoengineeringrdquo ldquoPythonrdquoldquoScalardquo ldquobachelor degreerdquo) described above we received

Fig 3 Job Details for Top Three Companies

zero results using Googlersquos job search Indeedrsquos job searchproduced many results satisfying any one of the keywords iejobs that satisfy either ldquojQueryrdquo or ldquoengineeringrdquo or ldquoPythonrdquoor ldquoScalardquo or ldquobachelor degreerdquo Our results are not exhaustiveas we ran this experiment on a limited set of job postingsHowever when this approach is used on a larger dataset weobtain much better results and with a better user experience

B Analytics

Analytic-1 The top 40 technologies across all job postingsare shown in Fig 5a The top 40 skills that companies in therestaurant chain industry are looking for are shown in Fig5b The top 40 skills that companies in the staffing servicesindustry are looking for are shown in Fig 5c Top 40 skillsthat companies in the health services industry are looking forare shown in Fig 5d

Analytic-2 Top companies that use ldquoTableaurdquo and ldquoMon-goDBrdquo technologies are shown in Fig 6a and top companieswhich hire people with ldquomaster degreerdquo and have ldquoJavardquoprogramming skills are shown in Fig 6b

V CONCLUSIONS AND FUTURE WORK

In this paper we have discussed a methodology that en-hances the job searching experience of users by adding skillset and company attribute filters Job seekers can pinpointjobs by filtering on relevant skill sets technologies numberof employees micro industries and many other attributes Thefiltered jobs are ranked using a relevance score derived froma weighted combination of skill sets and external factors ofcompanies as described in III-F We have also developedadvanced analytics on the data as shown in IV-B

We can additionally enhance the analytics with a temporalaspect by analyzing collected jobs information over time andidentifying trends in the skill set requirements for differentindustries We can also add more filters such as additionalsocial attributes that identify companiesrsquo social presence on

Fig 4 Top 40 Companies with Contacts

(a) Top 40 Technologies(b) Top 40 Skills in theRestaurant Chain Industry

(c) Top 40 Skills in theStaffing Services Industry

(d) Top 40 Skills in theHealth Services Industry

Fig 5 Analytic -1

(a) Top companies that usesldquoTableaurdquo and ldquoMongoDBrdquo

(b) Top recruiters that requirea person who knows ldquojavardquoand has a ldquomaster degreerdquo

Fig 6 Analytic - 2

the web We can also add higher level attributes that arederived from a combination of existing attributes For examplea higher level attribute such as ldquoMarketing Sophisticationrdquo canbe derived by a combination of social presence and advertisingtechnologies attributes

ACKNOWLEDGMENT

We would like to thank Everstring for generously providingthe datasets needed for our research and assisting us throughout this project and being a source of motivation and knowl-edge We would also like to thank Everstringrsquos HPC (HighPerformance Computing) Group for providing us with constanttechnical support over their Hadoop cluster

REFERENCES

[1] Joachims and Thorsten A Probabilistic Analysis of the Rocchio Al-gorithm with TF-IDF for Text Categorization No CMU-CS-96-118Carnegie-Mellon Univ Pittsburgh Pa Dept of Computer Science 1996

[2] W T Yih and C Meek ldquoImproving similarity measures for short seg-ments of textrdquo in Proceedings of the National Conference on ArtificialIntelligence vol 22 no 2 Menlo Park CA Cambridge MA LondonAAAI Press MIT Press 1999 2007

[3] K Jasmeen and V Gupta ldquoEffective approaches for extraction ofkeywordsrdquo IJCSI International Journal of Computer Science Issues 762010

[4] K Zhang et al ldquoKeyword extraction using support vector machinerdquoAdvances in Web-Age Information Management pp 85-96 2006

[5] H Bao and D Zhen ldquoAn Extended Keyword Extraction MethodrdquoPhysics Procedia vol 24 pp 1120-1127 2012

[6] Y Yorozu M Hirano K Oka and Y Tagawa ldquoElectron spectroscopystudies on magneto-optical media and plastic substrate interfacerdquo IEEETransl J Magn Japan vol 2 pp 740ndash741 August 1987 [Digests 9thAnnual Conf Magnetics Japan p 301 1982]

[7] Jussi Myllymaki ldquoEffective Web Data Extraction with Standard XMLTechnologiesrdquo

[8] Qiaoling Liu Faizan Javed and Matt Mcnair CompanyDepotldquordquo Em-ployer Name Normalization in the Online Recruitment Industryrdquo

[9] Fernndez and Miriam et al ldquoSemantically enhanced Information Re-trieval an ontology-based approachrdquo Web semantics Science servicesand agents on the worldwide web 94 pp 434-452 2011

[10] K Soner et al ldquoAn ontology-based retrieval system using semanticindexingrdquo Information Systems vol 37 no 4 pp 294-305 2012

[11] J J Feng et al ldquoKeyword extraction based on sequential pattern min-ingrdquo in Proceedings of the Third International Conference on InternetMultimedia Computing and Service ACM 2011

[12] F Liu F F Liu and Y Liu ldquoA supervised framework for keywordextraction from meeting transcriptsrdquo Audio Speech and LanguageProcessing IEEE Transactions on vol19 no 3 pp 538-548 2011

[13] S H Xu S H Yang and F CM Lau ldquoKeyword extraction and headlinegeneration using novel word featuresrdquo in Proc of the Twenty-FourthAAAI Conference on Artificial Intelligence 2010

[14] I Keisuke and N Mc Cracken ldquoAutomated Keyword Extraction ofLearning Materials Using Semantic Relationsrdquo 2010

[15] C W Bruce D Metzler and T Strohman Search engines Informationretrieval in practice Addison-Wesley 2010

[16] V P Schaik and J Ling ldquoAn integrated model of interaction experiencefor information retrieval in a Web-based encyclopaediardquo Interacting withComputers vol 23 no1 PP18-32 2011

[17] L Yu et al ldquoConcepts Similarity Algorithm Based on Google and KLDistancerdquo Jisuanji Gongcheng Computer Engineering vol 37 no 192011

[18] P I Chen S J Lin and Y C Chu ldquoUsing Google latent semanticdistance to extract the most relevant informationrdquo Expert Systems withApplications vol 38 no 6 pp 7349-7358 2011

[19] P I Chen and S J Lin ldquoAutomatic keyword prediction using Googlesimilarity distancerdquo Expert Systems with Applications vol 37 no3pp 1928-1938 2010

[20] X M Qian X H Hua and X S Hou ldquoTag filtering based onsimilar compatible principlerdquo Image Processing (ICIP) 2012 19th IEEEInternational Conference on IEEE 2012

[21] P I Chen and S J Lin ldquoWord AdHoc network using Google coredistance to extract the most relevant informationrdquo Knowledge-BasedSystems vol 24 no 3 pp 393-405 2011

[22] Ioannis Antonellis and Efstratios Gallopoulos ldquoExploring term-document matrices from matrix models in text miningrdquo

[23] Emmanuel Malherbe and Marie-Aude Aufaure ldquoBridge the terminologygap between recruiters and candidates A multilingual skills base builtfrom social media and linked datardquo

[24] Simon Hughes and his team ldquohttpwwwdicecomskillsrdquo[25] Kush R VarshneyJun Wang Aleksandra MojsilovicDongping Fang

and John H Bauer ldquoPredicting and Recommending Skills in the SocialEnterpriserdquo

[26] Wenjun ZhouYun ZhuFaizan Javed ldquoQuantifying skill relevance to jobtitlesrdquo

[27] Nikita SpirinKarrie Karahalios ldquoUnsupervised Approach to GenerateInformative Structured Snippets for Job Search Enginesrdquo

[28] Khalifeh AlJadda Mohammed KorayemCamilo Ortiz Chris RussellDavid Bernal Lamar Payson Scott Brown and Trey Grainger ldquoAug-menting Recommendation Systems Using a Model of Semantically-related Terms Extracted from User Behaviorrdquo

[29] indeedcom rsquoIndeed Job Search engine APIrsquo2017 [Online] Availablehttpsmarketmashapecomindeedindeed [Accessed 1- Jun- 2017]

[30] googlecom rsquoGoogle launches its AI-powered jobs search enginersquo2017 [Online] Available httpstechcrunchcom20170620google-launches-its-ai-powered-jobs-search-engine [Accessed 1- Aug- 2017]

[31] everstringcom rsquoEverstring APIrsquo 2017 [Online] Availablehttpsapieverstringcomv1companiesdiscover [Accessed 1- Jun-2017]

[32] facebookcom rsquoFacebook job Search Pagersquo 2017[Online] Availablehttpstechcrunchcom20161107jobbook [Accessed 15- July-2017]

[33] alexacom rsquoFind Website Traffic Statistics and Analyticsrsquo Availablehttpwwwalexacomsiteinfo[Accessed 1- Jun- 2017]

[34] Time Tech rsquoGoogle Wants to Help You Search for a New Jobrsquo2017 [Online] Available httptimecom4824968google-job-search[Accessed 20- Jun- 2017]

[35] Example Job pagehttpswwwindeedcomrcclkjk=d93199bf4c06f3bampfccid=d85b448de778e20c

JobSearchEngine

Data Sources

Machine Learning

Applications

Ranking Search ResultsCompute Engine

Advanced Job Search App Engine

Getting Relevant ContactsApp Engine

Crawled Datajob Postings

Professional social network DataSkill Set

Indeed DataJob Postings

Analytics

Spark MLlibData Processing

ETLDataflow

Transform Data

Normalized DataHDFS Storage

Combined Sources

Skill Set Ranking Machine Learning

Design Diagram Job Search Relevance

Domain Name GenerationMachine Learning

Everstring DataCompany Features

Everstring DataContact Features

Fig 1 Design Diagram - Job Search Engine

job hiring market are developed and shown in this paper Thedatabase generated can be used to find the job postings whichexactly match the usersrsquo requirement and also give the userswith the contacts of the relevant people currently working inthe companies of the corresponding job postingsThe process is described below

1) Extract the job postings and skills data from indeedcompany job pages and professional social networks[23]

2) Extract useful tokens of data and store them in dis-tributed database - ETL(Extract Transform and Load)

3) Normalize clean the data as described in III-B Obtainthe domain name from company name as described inIII-C

4) Extract and rank the skill set required for a job asdescribed in III-D and III-E

5) Merge with the Everstring Data using API to extractuseful Insights

6) Run user specific search queries on the database toreceive relevant job postings in a ranked order IV-A

7) Perform analytics IV-B

A Data Extraction

We constructed a web crawler to crawl the job pages of com-panies to extract the company names urls job descriptionsjob titles and company address This was done by exploitingregularities in the html structure of the companies websiteand by constructing extracting functions to automatically ex-tract company names and descriptions of jobs from arbitrarywebsites Additionally we used Indeedrsquos API to fetch the jobpostings with all the necessary features Here we didnrsquot crawlthe entire job posting web page instead we crawl only theuseful information that is required such as job descriptiontitles company name and address This is in contrary with thetraditional web crawlers which crawl the entire website andlater extract the useful features from the entire unstructuredwebsite data This crawl was done by using an XML based

approach and viewing the task of data extraction as a multi-step process where the goal extends far beyond simpleldquoscreenscrapingrdquo as mentioned in [7] Navigation rules and extractionrules were optimized by hand to produce semi structured dataThe skill sets mentioned in the professional social networksare extracted by leveraging the Linking Open Data projectDBpedia [23] to generate a set of 750k unique skills

B Data Preprocessing

1) Skills Data By generating the co-occurrence countsof the skills [23] we can filter the skills based on a countthreshold which is decided based on the frequency and thenumber of words in the skill We then generate lower casedskills and apply basic normalization techniques on the skillsusing several common patterns For example replacing ldquoamprdquoor ldquo+rdquo with ldquoandrdquo unless ldquorampdrdquo removing the dash in ldquoe-mailrdquo and running basic lemmatization on the skills Whilenormalizing we keep the original lower cased skills and thenormalized skills in a sequence For example if the input isldquoe-mailrdquo we generate a sequence of the original skill and thenormalized skill - Seq(ldquoe-mailrdquoldquoemailrdquo) We then generate alemmatized skill (standard word in WordNet) using the lowercased skill To generate the lemmatized skill we parse the skillinto its part of speech(POS) to obtain the word Ngrams of theskill We then replace any Ngram with the one that is presentin WordNet If the Ngram is not present in WordNet the Ngramis not replaced Using the lemmatized Ngrams we combinethe Ngrams back to generate the lemmatized skill LeveragingEverstringrsquos Human Intelligence Tasks (HITs) system an in-house on-demand workforce platform we filter the 1000 mostfrequently occurred lemmatized skills For example wordslike ldquoteamrdquo ldquoknowledgerdquoldquoinformationrdquo which occurred morefrequently in the dataset and are removed from the skill set asthey act as outliers in our skill set

We construct a lemma dictionary using the lemmatizedskills and its corresponding sequence of normalized andlower cased skills The lemma dictionary is a map from thenormalized and original skill to the lemmatized skill Thenormalized and original skill will be the key in the mapThe sequence key is split to generate two different keys Forexample Seq(ldquoArdquoldquoBrdquo) map to ldquoCrdquo becomes a map fromldquoArdquo rarr ldquoCrdquoand ldquoBrdquo rarr ldquoCrdquo This generates a dictionarythat maps the original and normalized skills to the lemma-tized skills The total number of distinct lemmatized skills isreduced from 750k to 73k after filtering and lemmatization ofskillsFor Example ldquocnetrdquo ldquoc netrdquoldquoc amp netrdquo and ldquocand netrdquo are mapped to ldquoc and netrdquo ldquosystems installa-tionsrdquoldquosystem installationsrdquoldquosystems installationrdquo and ldquosys-tem installingrdquo are mapped to ldquosystem installationrdquo

2) Job Postings Data In this section we discuss multipleinformation extraction (IE) steps performed on the job postingsdata All IE steps are performed on the raw text of the jobtitles company names and job descriptions to retrieve usefuland structured information and to generate and rank the skillsets to take the job search process to a much finer level

bull Title Normalization and Parsing The title string in itsnatural language form is first normalized and then parsedto obtain the management level and the department(s) itfalls under The title normalization steps are1 Replace specific characters such as spacing out com-mas and handling special characters2 Remove level information eg strip out roman numeraltails ldquolevel 1rdquo etc Apply Ngram substitutions egV P rarr V icePresident eminusmailrarr email3 Add acronym forms with periods after each charactereg ceorarr ceoAt a high level we apply character level(1) sequencelevel(2) and token level(3) transformations to normalizethe title In between these steps we use regex to split thetitle at different levels to identify the title NgramtokensWe parse the normalized title to obtain its managementlevel and department(s) The title is classified into oneof five different management levels (C-Level VP-LevelDirector Manager Non-Manager) and one or more de-partments (ldquoAdministrativerdquo ldquoComputing amp ITrdquo ldquoEngi-neeringrdquo ldquoEducatorrdquo ldquoFinancerdquo ldquoHRrdquo ldquoMarketingrdquo andmany others) This parsing is performed using a manuallygenerating mapping from title Ngrams to managementlevels and departments

bull Company Name Normalizer The company name stringin its natural language form is normalized to a form thatfacilitates matching to a website1 Convert the company name to lowercase and replaceldquorsquosrdquo with ldquosrdquo eg ldquoMacyprimesrdquorarr ldquomacysrdquo and convertnon-alphanumeric characters to spaces2 Generate a set of suffixes and prefixes that are com-monly used in company names like ldquollcrdquo ldquoltdrdquo ldquocorprdquoldquoincrdquo ldquocordquo and many others and use this set to drop theoccurrences of these words in the company name3 Generate a set of stop words that are not useful torecognize a company such as ldquotechnologiesrdquo ldquomanage-mentrdquo ldquoservicerdquo ldquopvtrdquo ldquogrouprdquo ldquosolutionsrdquo and manyothers Use this set to drop the occurrences of these wordsin the company names irrespective of their position Thismethod is inspired by the CompanyDepot [8] system

bull Job Description Cleaning To normalize the job descrip-tions we use the same normalization schema as the oneused to normalize the skills data

bull BOW Generation Using Skill Sets The lemma dictionarywhich maps the lemma to the original and normalizedskill is constructed using the skills mentioned in profes-sional social networks We use this lemma dictionary tocount the occurrences of the normalized or the originalskill in the job descriptions and generate a map fromthe lemma skill to its count keeping the track of theoriginal form of the skill Here we create a map fromthe lemmatized skill to the struct of the total lemma countand the map of original or normalized skill Ngram andits count

C Company Name to Website

In order to utilize company firmographic APIs that areavailable on the web we had to convert the normalized com-pany name associated with each job posting to their respectivecompany website To perform this conversion we leveragedEverStringrsquos Company Name to Website service which takesin inputs parameter such as name location (parsed or unparsedstreet city state zip) and outputs the website associatedwith the input with a confidence score EverStringrsquos CompanyName to Website Service was generated by triangulating bothcrawled results as well as multiple purchased data sources togenerate a comprehensive alias table for fast query Using thisservice we were able to convert company names like ldquoAmazonDriverdquo ldquoAmazon Web Servicesrdquo ldquoAmazon Primerdquo ldquoAmazon-Freshrdquo ldquoAmazon HVHrdquoldquoAmazon Corporate LLCrdquo ldquoAmazonLogisticsrdquoldquoAmazon Web Services Incrdquo ldquoAmazoncomdedcLLCrdquoldquoAmazon FulfillmentrdquoldquoAmazon Fulfillment servicesrdquoldquoAmazonrdquo to the companyrsquos website amazoncom with highconfidence scores

D TF-IDF Generation

Document Frequency Generation We begin by countingdocument (here a document refers to a job posting) frequenciesfor all unique skills (lemmas original and normalized skills)present in the skills lemma dictionary computed in III-B1We compute document frequencies for skill lemmas andfilter out lemmas that do not satisfy a minimum documentfrequency threshold For the remaining valid lemmas we addthe original Ngram forms (if different) to the list of documentfrequency counts so we have a unified dictionary whichmaps all the skills in the documents to its count Documentswith zero document length (sum of all the counts of theskills - docLen) are removed from the corpus Reservedkeyword ldquonDocsrdquo is set to represent the number of validdocuments in our corpus which helps in computing theinverse-document-frequency laterLTU term weighting scheme(TF-IDF) The term frequenciesare generated for each document job posting by countingthe number of occurrences of the lemma skill as described inthe ldquoBOW generationrdquo section above The weighting for eachskill in a document is generated using the following formula

LTU =(log2 (tf) + 1) log2

(nDocs

df

)(08 + 02 docLen

avgDocLen )

where tf (Term Frequency) is the number of occurrences ofthis term (the skill for which we are computing the weight) inthe current document docLen(Document Length) is total count(with repetitions) of terms present in the current documentnDocs (Number Documents) is the total number of documents(job postings) in the database avgDocLen (Average DocumentLength) is the average number (with repetitions) of termspresent across all documents df (Document Frequency) is thenumber of documents containing this term LTU weightinguses a pivoted document length normalization scheme basedon the intuition that words that appear the same number

of times in shorter documents should be considered morerelevant than in longer documents This weighting schemaalso incorporates sub-linear term frequency scaling Finallythe scaled TF-IDF is generated for each document by scalingthe maximum TF-IDF value of a skill to 1

E Weights Generation

Ranking the skills using just the TF-IDF score did notproduce good results as we have only taken the job descrip-tions into consideration for generating this score While thejob description contains an extensive amount of informationabout the company the benefits they provide is mixed Theprimary concern is much of the information acts as a noisein our system This drives us to include one more additionaldimensions for a better ranking In order to achieve this weuse the job title to generate a weighting schema on top ofthe TF-IDF that we have computed previously Intuitively wewant a skill to be weighted higher if it occurs more frequentlyunder a title Ngram If a particular skill is very common inthe dataset then it should be weighted lower For example jobpostings with ldquoSoftwarerdquo in the title will weigh the skills likeldquoCrdquoldquoC++rdquoldquoJavardquoldquoOOPrdquo higher as they frequently occur inthe job posting with ldquoSoftwarerdquo in their title They will notoccur in many non technical job postings (a high percentageof job postings in our corpus) which boosts the probabilityof having these skills given ldquoSoftwarerdquo in the title In thisway we have added one more dimension to weigh the skillsby considering the titles of the jobs The weighting schema ismathematically explained using a formula which is explainedin the following sections

1) Title Ngram Generation Initially we normalize the titleand remove all punctuations present in the title We then splitthe title to generate a set of all unigrams present in the titlesThis set is filtered based on the occurrence frequency and stopwords We then generate bigrams from the title by looking atthe sequence of two words in the original normalized title andfiltering out the bigrams by checking if it is present in theoriginal normalized title Eg If we have ldquoBig Data SoftwareEngineerrdquo as a title we look for all bigram sequences (ldquoBigDatardquoldquoData Softwarerdquo and ldquoSoftware Engineerrdquo) in the titleand add the bigrams that are present in the title to generate thefinal list of title Ngrams In this example ldquoSoftware Engineerrdquowill be added to the title Ngrams list After generating this listwe split the normalized title to obtain the unigrams bigramsand filter out the grams that are not present in the title toobtain the title Ngram list for each job title

2) Weight Matrix Generation Now that we have an arrayof title Ngrams for every title in the corpus we use thisinformation to rank the skills that are extracted for eachjob posting (ldquo BOW Generationrdquo in the section above) bygenerating a weight for every skill present in a job postingusing a count matrix between the skills and the title Ngramsacross all the documents The count matrix [22] is computedby generating the term frequency of a particular skill presentin all job descriptions that has a particular title Ngram in itstitle Eg we obtain a count array (of length equal to number

of unique skills) for every unique title Ngram This generatesa matrix giving the count of the occurrences of all skillscorresponding to a particular title Ngram Using this countmatrix we compute the weight for every skill in a job postingby adopting the following formula

weight(skill) =prob(skill|titleNgram)

prob(skill)

The above weighting formula supports our intuition that theskill which is more common across all the documents willbe weighted lower and the skill is weighted higher if theprobability of finding it in all the job descriptions which has aparticular title Ngram is higher For a particular skill in a jobposting we obtain a different weight for each title Ngram ofa job posting because each title in a job posting has multipletitle Ngrams To generate a single weight for each skill in ajob posting we average all of the weights computed for thisskill and all of the job title Ngrams

3) Final Score Generation In the previous section wecomputed the weights for all of the skills in a job postingThis weight is multiplied with the TF-IDFLTU computedpreviously to obtain a score for a particular skill in a jobposting This score and many other external factors about thecompany are used to rank the skills corresponding to a jobposting

F Query Results Ranking

After processing the query the search results are rankedbased on the following formula

Rank(document) = AttributeScore lowastAvg(weight(skill))

Avg(weight(skill)) is the average of all the weights III-E of theskills corresponding to a job posting As we have a weightfor all the skills for each job posting we take the skills givenby the user and compute the aggregated score which is theaverage of user given skills weight

AttributeScore = feedback lowast af lowast ef lowast nlf lowast cks

where af is the alexa Factor [33] ef is the Employee Factor(Afactor computed using the information of the number ofemployees in the company) nlf is the Number of LemmasFactor cks = 2

radicscore where score is the micro industry

keyword score and feedback is the score defined by the numberof user clicks

IV RESULTS

In this section we will show an example of how a jobseeker can search for relevant jobs based on the personrsquosskill set the desired industry space the preferred technologystack and many other skill and company attributes The jobseeker can then query for relevant contacts within the companywhich helps the job seeker to connect and network We thencompared our search results with other job search websitessuch as Indeed and Google

In addition this section presents examples of enhancedanalytics including analysis of the most common skill sets

required by ldquostaffing servicesrdquoldquohealth servicesrdquo and ldquorestau-rant chainrdquo industries the most desired technologies in the USjob market and presenting the top recruiting companies thatare looking for prospects with specific skill sets (ldquoJavardquo andldquoMasters Degreerdquo) or uses specific technologies (ldquoTableaurdquoand ldquoMongoDBrdquo)

A Job Search Results

Query-1 A User with a bachelorrsquos degree and has Pythonand Scala programming skills wants to search for the jobs incompanies which uses jQuery technology wants to work inthe ldquoengineeringrdquo vertical with companies whose revenue isgreat than 1 Million USD (the revenue that we show in thefollowing results are in terms of 1000 USD ) and the numberof employees to be between 50 and 200

The top 40 companies that we identified with the abovesearch query are shown in Fig 2

Fig 2 Top 40 Companies Found Using Query-1

Showing detailed job results for the top three companiesfrom Fig 2 in ranked order is shown in Fig 3 To clarifyon few results which may look odd we have results likeldquoSenior Full stack Java Developerrdquo shown up for the abovequery When looked into the job description [35] (Job Keyd93199bf4c06f3b4) we have the following statement ldquoStrongprofessional experience with Java and at least one otherlanguage - Python Go Scala etcrdquo So this supports our searchquery Here we can also show the ranked job postings basedon the query but here we want to present the results based onthe company similar to topic search engine

The contact information of the recruiters of the top 40companies in the Fig 2 are shown in Fig 4 Consideringthe privacy violation we are not showing the phone numbersand the emails of the recruiters in this paper

No job search website supports querying with number ofemployees and revenue When we searched for a combinationof the five keywords (ldquojQueryrdquo ldquoengineeringrdquo ldquoPythonrdquoldquoScalardquo ldquobachelor degreerdquo) described above we received

Fig 3 Job Details for Top Three Companies

zero results using Googlersquos job search Indeedrsquos job searchproduced many results satisfying any one of the keywords iejobs that satisfy either ldquojQueryrdquo or ldquoengineeringrdquo or ldquoPythonrdquoor ldquoScalardquo or ldquobachelor degreerdquo Our results are not exhaustiveas we ran this experiment on a limited set of job postingsHowever when this approach is used on a larger dataset weobtain much better results and with a better user experience

B Analytics

Analytic-1 The top 40 technologies across all job postingsare shown in Fig 5a The top 40 skills that companies in therestaurant chain industry are looking for are shown in Fig5b The top 40 skills that companies in the staffing servicesindustry are looking for are shown in Fig 5c Top 40 skillsthat companies in the health services industry are looking forare shown in Fig 5d

Analytic-2 Top companies that use ldquoTableaurdquo and ldquoMon-goDBrdquo technologies are shown in Fig 6a and top companieswhich hire people with ldquomaster degreerdquo and have ldquoJavardquoprogramming skills are shown in Fig 6b

V CONCLUSIONS AND FUTURE WORK

In this paper we have discussed a methodology that en-hances the job searching experience of users by adding skillset and company attribute filters Job seekers can pinpointjobs by filtering on relevant skill sets technologies numberof employees micro industries and many other attributes Thefiltered jobs are ranked using a relevance score derived froma weighted combination of skill sets and external factors ofcompanies as described in III-F We have also developedadvanced analytics on the data as shown in IV-B

We can additionally enhance the analytics with a temporalaspect by analyzing collected jobs information over time andidentifying trends in the skill set requirements for differentindustries We can also add more filters such as additionalsocial attributes that identify companiesrsquo social presence on

Fig 4 Top 40 Companies with Contacts

(a) Top 40 Technologies(b) Top 40 Skills in theRestaurant Chain Industry

(c) Top 40 Skills in theStaffing Services Industry

(d) Top 40 Skills in theHealth Services Industry

Fig 5 Analytic -1

(a) Top companies that usesldquoTableaurdquo and ldquoMongoDBrdquo

(b) Top recruiters that requirea person who knows ldquojavardquoand has a ldquomaster degreerdquo

Fig 6 Analytic - 2

the web We can also add higher level attributes that arederived from a combination of existing attributes For examplea higher level attribute such as ldquoMarketing Sophisticationrdquo canbe derived by a combination of social presence and advertisingtechnologies attributes

ACKNOWLEDGMENT

We would like to thank Everstring for generously providingthe datasets needed for our research and assisting us throughout this project and being a source of motivation and knowl-edge We would also like to thank Everstringrsquos HPC (HighPerformance Computing) Group for providing us with constanttechnical support over their Hadoop cluster

REFERENCES

[1] Joachims and Thorsten A Probabilistic Analysis of the Rocchio Al-gorithm with TF-IDF for Text Categorization No CMU-CS-96-118Carnegie-Mellon Univ Pittsburgh Pa Dept of Computer Science 1996

[2] W T Yih and C Meek ldquoImproving similarity measures for short seg-ments of textrdquo in Proceedings of the National Conference on ArtificialIntelligence vol 22 no 2 Menlo Park CA Cambridge MA LondonAAAI Press MIT Press 1999 2007

[3] K Jasmeen and V Gupta ldquoEffective approaches for extraction ofkeywordsrdquo IJCSI International Journal of Computer Science Issues 762010

[4] K Zhang et al ldquoKeyword extraction using support vector machinerdquoAdvances in Web-Age Information Management pp 85-96 2006

[5] H Bao and D Zhen ldquoAn Extended Keyword Extraction MethodrdquoPhysics Procedia vol 24 pp 1120-1127 2012

[6] Y Yorozu M Hirano K Oka and Y Tagawa ldquoElectron spectroscopystudies on magneto-optical media and plastic substrate interfacerdquo IEEETransl J Magn Japan vol 2 pp 740ndash741 August 1987 [Digests 9thAnnual Conf Magnetics Japan p 301 1982]

[7] Jussi Myllymaki ldquoEffective Web Data Extraction with Standard XMLTechnologiesrdquo

[8] Qiaoling Liu Faizan Javed and Matt Mcnair CompanyDepotldquordquo Em-ployer Name Normalization in the Online Recruitment Industryrdquo

[9] Fernndez and Miriam et al ldquoSemantically enhanced Information Re-trieval an ontology-based approachrdquo Web semantics Science servicesand agents on the worldwide web 94 pp 434-452 2011

[10] K Soner et al ldquoAn ontology-based retrieval system using semanticindexingrdquo Information Systems vol 37 no 4 pp 294-305 2012

[11] J J Feng et al ldquoKeyword extraction based on sequential pattern min-ingrdquo in Proceedings of the Third International Conference on InternetMultimedia Computing and Service ACM 2011

[12] F Liu F F Liu and Y Liu ldquoA supervised framework for keywordextraction from meeting transcriptsrdquo Audio Speech and LanguageProcessing IEEE Transactions on vol19 no 3 pp 538-548 2011

[13] S H Xu S H Yang and F CM Lau ldquoKeyword extraction and headlinegeneration using novel word featuresrdquo in Proc of the Twenty-FourthAAAI Conference on Artificial Intelligence 2010

[14] I Keisuke and N Mc Cracken ldquoAutomated Keyword Extraction ofLearning Materials Using Semantic Relationsrdquo 2010

[15] C W Bruce D Metzler and T Strohman Search engines Informationretrieval in practice Addison-Wesley 2010

[16] V P Schaik and J Ling ldquoAn integrated model of interaction experiencefor information retrieval in a Web-based encyclopaediardquo Interacting withComputers vol 23 no1 PP18-32 2011

[17] L Yu et al ldquoConcepts Similarity Algorithm Based on Google and KLDistancerdquo Jisuanji Gongcheng Computer Engineering vol 37 no 192011

[18] P I Chen S J Lin and Y C Chu ldquoUsing Google latent semanticdistance to extract the most relevant informationrdquo Expert Systems withApplications vol 38 no 6 pp 7349-7358 2011

[19] P I Chen and S J Lin ldquoAutomatic keyword prediction using Googlesimilarity distancerdquo Expert Systems with Applications vol 37 no3pp 1928-1938 2010

[20] X M Qian X H Hua and X S Hou ldquoTag filtering based onsimilar compatible principlerdquo Image Processing (ICIP) 2012 19th IEEEInternational Conference on IEEE 2012

[21] P I Chen and S J Lin ldquoWord AdHoc network using Google coredistance to extract the most relevant informationrdquo Knowledge-BasedSystems vol 24 no 3 pp 393-405 2011

[22] Ioannis Antonellis and Efstratios Gallopoulos ldquoExploring term-document matrices from matrix models in text miningrdquo

[23] Emmanuel Malherbe and Marie-Aude Aufaure ldquoBridge the terminologygap between recruiters and candidates A multilingual skills base builtfrom social media and linked datardquo

[24] Simon Hughes and his team ldquohttpwwwdicecomskillsrdquo[25] Kush R VarshneyJun Wang Aleksandra MojsilovicDongping Fang

and John H Bauer ldquoPredicting and Recommending Skills in the SocialEnterpriserdquo

[26] Wenjun ZhouYun ZhuFaizan Javed ldquoQuantifying skill relevance to jobtitlesrdquo

[27] Nikita SpirinKarrie Karahalios ldquoUnsupervised Approach to GenerateInformative Structured Snippets for Job Search Enginesrdquo

[28] Khalifeh AlJadda Mohammed KorayemCamilo Ortiz Chris RussellDavid Bernal Lamar Payson Scott Brown and Trey Grainger ldquoAug-menting Recommendation Systems Using a Model of Semantically-related Terms Extracted from User Behaviorrdquo

[29] indeedcom rsquoIndeed Job Search engine APIrsquo2017 [Online] Availablehttpsmarketmashapecomindeedindeed [Accessed 1- Jun- 2017]

[30] googlecom rsquoGoogle launches its AI-powered jobs search enginersquo2017 [Online] Available httpstechcrunchcom20170620google-launches-its-ai-powered-jobs-search-engine [Accessed 1- Aug- 2017]

[31] everstringcom rsquoEverstring APIrsquo 2017 [Online] Availablehttpsapieverstringcomv1companiesdiscover [Accessed 1- Jun-2017]

[32] facebookcom rsquoFacebook job Search Pagersquo 2017[Online] Availablehttpstechcrunchcom20161107jobbook [Accessed 15- July-2017]

[33] alexacom rsquoFind Website Traffic Statistics and Analyticsrsquo Availablehttpwwwalexacomsiteinfo[Accessed 1- Jun- 2017]

[34] Time Tech rsquoGoogle Wants to Help You Search for a New Jobrsquo2017 [Online] Available httptimecom4824968google-job-search[Accessed 20- Jun- 2017]

[35] Example Job pagehttpswwwindeedcomrcclkjk=d93199bf4c06f3bampfccid=d85b448de778e20c

bull Title Normalization and Parsing The title string in itsnatural language form is first normalized and then parsedto obtain the management level and the department(s) itfalls under The title normalization steps are1 Replace specific characters such as spacing out com-mas and handling special characters2 Remove level information eg strip out roman numeraltails ldquolevel 1rdquo etc Apply Ngram substitutions egV P rarr V icePresident eminusmailrarr email3 Add acronym forms with periods after each charactereg ceorarr ceoAt a high level we apply character level(1) sequencelevel(2) and token level(3) transformations to normalizethe title In between these steps we use regex to split thetitle at different levels to identify the title NgramtokensWe parse the normalized title to obtain its managementlevel and department(s) The title is classified into oneof five different management levels (C-Level VP-LevelDirector Manager Non-Manager) and one or more de-partments (ldquoAdministrativerdquo ldquoComputing amp ITrdquo ldquoEngi-neeringrdquo ldquoEducatorrdquo ldquoFinancerdquo ldquoHRrdquo ldquoMarketingrdquo andmany others) This parsing is performed using a manuallygenerating mapping from title Ngrams to managementlevels and departments

bull Company Name Normalizer The company name stringin its natural language form is normalized to a form thatfacilitates matching to a website1 Convert the company name to lowercase and replaceldquorsquosrdquo with ldquosrdquo eg ldquoMacyprimesrdquorarr ldquomacysrdquo and convertnon-alphanumeric characters to spaces2 Generate a set of suffixes and prefixes that are com-monly used in company names like ldquollcrdquo ldquoltdrdquo ldquocorprdquoldquoincrdquo ldquocordquo and many others and use this set to drop theoccurrences of these words in the company name3 Generate a set of stop words that are not useful torecognize a company such as ldquotechnologiesrdquo ldquomanage-mentrdquo ldquoservicerdquo ldquopvtrdquo ldquogrouprdquo ldquosolutionsrdquo and manyothers Use this set to drop the occurrences of these wordsin the company names irrespective of their position Thismethod is inspired by the CompanyDepot [8] system

bull Job Description Cleaning To normalize the job descrip-tions we use the same normalization schema as the oneused to normalize the skills data

bull BOW Generation Using Skill Sets The lemma dictionarywhich maps the lemma to the original and normalizedskill is constructed using the skills mentioned in profes-sional social networks We use this lemma dictionary tocount the occurrences of the normalized or the originalskill in the job descriptions and generate a map fromthe lemma skill to its count keeping the track of theoriginal form of the skill Here we create a map fromthe lemmatized skill to the struct of the total lemma countand the map of original or normalized skill Ngram andits count

C Company Name to Website

In order to utilize company firmographic APIs that areavailable on the web we had to convert the normalized com-pany name associated with each job posting to their respectivecompany website To perform this conversion we leveragedEverStringrsquos Company Name to Website service which takesin inputs parameter such as name location (parsed or unparsedstreet city state zip) and outputs the website associatedwith the input with a confidence score EverStringrsquos CompanyName to Website Service was generated by triangulating bothcrawled results as well as multiple purchased data sources togenerate a comprehensive alias table for fast query Using thisservice we were able to convert company names like ldquoAmazonDriverdquo ldquoAmazon Web Servicesrdquo ldquoAmazon Primerdquo ldquoAmazon-Freshrdquo ldquoAmazon HVHrdquoldquoAmazon Corporate LLCrdquo ldquoAmazonLogisticsrdquoldquoAmazon Web Services Incrdquo ldquoAmazoncomdedcLLCrdquoldquoAmazon FulfillmentrdquoldquoAmazon Fulfillment servicesrdquoldquoAmazonrdquo to the companyrsquos website amazoncom with highconfidence scores

D TF-IDF Generation

Document Frequency Generation We begin by countingdocument (here a document refers to a job posting) frequenciesfor all unique skills (lemmas original and normalized skills)present in the skills lemma dictionary computed in III-B1We compute document frequencies for skill lemmas andfilter out lemmas that do not satisfy a minimum documentfrequency threshold For the remaining valid lemmas we addthe original Ngram forms (if different) to the list of documentfrequency counts so we have a unified dictionary whichmaps all the skills in the documents to its count Documentswith zero document length (sum of all the counts of theskills - docLen) are removed from the corpus Reservedkeyword ldquonDocsrdquo is set to represent the number of validdocuments in our corpus which helps in computing theinverse-document-frequency laterLTU term weighting scheme(TF-IDF) The term frequenciesare generated for each document job posting by countingthe number of occurrences of the lemma skill as described inthe ldquoBOW generationrdquo section above The weighting for eachskill in a document is generated using the following formula

LTU =(log2 (tf) + 1) log2

(nDocs

df

)(08 + 02 docLen

avgDocLen )

where tf (Term Frequency) is the number of occurrences ofthis term (the skill for which we are computing the weight) inthe current document docLen(Document Length) is total count(with repetitions) of terms present in the current documentnDocs (Number Documents) is the total number of documents(job postings) in the database avgDocLen (Average DocumentLength) is the average number (with repetitions) of termspresent across all documents df (Document Frequency) is thenumber of documents containing this term LTU weightinguses a pivoted document length normalization scheme basedon the intuition that words that appear the same number

of times in shorter documents should be considered morerelevant than in longer documents This weighting schemaalso incorporates sub-linear term frequency scaling Finallythe scaled TF-IDF is generated for each document by scalingthe maximum TF-IDF value of a skill to 1

E Weights Generation

Ranking the skills using just the TF-IDF score did notproduce good results as we have only taken the job descrip-tions into consideration for generating this score While thejob description contains an extensive amount of informationabout the company the benefits they provide is mixed Theprimary concern is much of the information acts as a noisein our system This drives us to include one more additionaldimensions for a better ranking In order to achieve this weuse the job title to generate a weighting schema on top ofthe TF-IDF that we have computed previously Intuitively wewant a skill to be weighted higher if it occurs more frequentlyunder a title Ngram If a particular skill is very common inthe dataset then it should be weighted lower For example jobpostings with ldquoSoftwarerdquo in the title will weigh the skills likeldquoCrdquoldquoC++rdquoldquoJavardquoldquoOOPrdquo higher as they frequently occur inthe job posting with ldquoSoftwarerdquo in their title They will notoccur in many non technical job postings (a high percentageof job postings in our corpus) which boosts the probabilityof having these skills given ldquoSoftwarerdquo in the title In thisway we have added one more dimension to weigh the skillsby considering the titles of the jobs The weighting schema ismathematically explained using a formula which is explainedin the following sections

1) Title Ngram Generation Initially we normalize the titleand remove all punctuations present in the title We then splitthe title to generate a set of all unigrams present in the titlesThis set is filtered based on the occurrence frequency and stopwords We then generate bigrams from the title by looking atthe sequence of two words in the original normalized title andfiltering out the bigrams by checking if it is present in theoriginal normalized title Eg If we have ldquoBig Data SoftwareEngineerrdquo as a title we look for all bigram sequences (ldquoBigDatardquoldquoData Softwarerdquo and ldquoSoftware Engineerrdquo) in the titleand add the bigrams that are present in the title to generate thefinal list of title Ngrams In this example ldquoSoftware Engineerrdquowill be added to the title Ngrams list After generating this listwe split the normalized title to obtain the unigrams bigramsand filter out the grams that are not present in the title toobtain the title Ngram list for each job title

2) Weight Matrix Generation Now that we have an arrayof title Ngrams for every title in the corpus we use thisinformation to rank the skills that are extracted for eachjob posting (ldquo BOW Generationrdquo in the section above) bygenerating a weight for every skill present in a job postingusing a count matrix between the skills and the title Ngramsacross all the documents The count matrix [22] is computedby generating the term frequency of a particular skill presentin all job descriptions that has a particular title Ngram in itstitle Eg we obtain a count array (of length equal to number

of unique skills) for every unique title Ngram This generatesa matrix giving the count of the occurrences of all skillscorresponding to a particular title Ngram Using this countmatrix we compute the weight for every skill in a job postingby adopting the following formula

weight(skill) =prob(skill|titleNgram)

prob(skill)

The above weighting formula supports our intuition that theskill which is more common across all the documents willbe weighted lower and the skill is weighted higher if theprobability of finding it in all the job descriptions which has aparticular title Ngram is higher For a particular skill in a jobposting we obtain a different weight for each title Ngram ofa job posting because each title in a job posting has multipletitle Ngrams To generate a single weight for each skill in ajob posting we average all of the weights computed for thisskill and all of the job title Ngrams

3) Final Score Generation In the previous section wecomputed the weights for all of the skills in a job postingThis weight is multiplied with the TF-IDFLTU computedpreviously to obtain a score for a particular skill in a jobposting This score and many other external factors about thecompany are used to rank the skills corresponding to a jobposting

F Query Results Ranking

After processing the query the search results are rankedbased on the following formula

Rank(document) = AttributeScore lowastAvg(weight(skill))

Avg(weight(skill)) is the average of all the weights III-E of theskills corresponding to a job posting As we have a weightfor all the skills for each job posting we take the skills givenby the user and compute the aggregated score which is theaverage of user given skills weight

AttributeScore = feedback lowast af lowast ef lowast nlf lowast cks

where af is the alexa Factor [33] ef is the Employee Factor(Afactor computed using the information of the number ofemployees in the company) nlf is the Number of LemmasFactor cks = 2

radicscore where score is the micro industry

keyword score and feedback is the score defined by the numberof user clicks

IV RESULTS

In this section we will show an example of how a jobseeker can search for relevant jobs based on the personrsquosskill set the desired industry space the preferred technologystack and many other skill and company attributes The jobseeker can then query for relevant contacts within the companywhich helps the job seeker to connect and network We thencompared our search results with other job search websitessuch as Indeed and Google

In addition this section presents examples of enhancedanalytics including analysis of the most common skill sets

required by ldquostaffing servicesrdquoldquohealth servicesrdquo and ldquorestau-rant chainrdquo industries the most desired technologies in the USjob market and presenting the top recruiting companies thatare looking for prospects with specific skill sets (ldquoJavardquo andldquoMasters Degreerdquo) or uses specific technologies (ldquoTableaurdquoand ldquoMongoDBrdquo)

A Job Search Results

Query-1 A User with a bachelorrsquos degree and has Pythonand Scala programming skills wants to search for the jobs incompanies which uses jQuery technology wants to work inthe ldquoengineeringrdquo vertical with companies whose revenue isgreat than 1 Million USD (the revenue that we show in thefollowing results are in terms of 1000 USD ) and the numberof employees to be between 50 and 200

The top 40 companies that we identified with the abovesearch query are shown in Fig 2

Fig 2 Top 40 Companies Found Using Query-1

Showing detailed job results for the top three companiesfrom Fig 2 in ranked order is shown in Fig 3 To clarifyon few results which may look odd we have results likeldquoSenior Full stack Java Developerrdquo shown up for the abovequery When looked into the job description [35] (Job Keyd93199bf4c06f3b4) we have the following statement ldquoStrongprofessional experience with Java and at least one otherlanguage - Python Go Scala etcrdquo So this supports our searchquery Here we can also show the ranked job postings basedon the query but here we want to present the results based onthe company similar to topic search engine

The contact information of the recruiters of the top 40companies in the Fig 2 are shown in Fig 4 Consideringthe privacy violation we are not showing the phone numbersand the emails of the recruiters in this paper

No job search website supports querying with number ofemployees and revenue When we searched for a combinationof the five keywords (ldquojQueryrdquo ldquoengineeringrdquo ldquoPythonrdquoldquoScalardquo ldquobachelor degreerdquo) described above we received

Fig 3 Job Details for Top Three Companies

zero results using Googlersquos job search Indeedrsquos job searchproduced many results satisfying any one of the keywords iejobs that satisfy either ldquojQueryrdquo or ldquoengineeringrdquo or ldquoPythonrdquoor ldquoScalardquo or ldquobachelor degreerdquo Our results are not exhaustiveas we ran this experiment on a limited set of job postingsHowever when this approach is used on a larger dataset weobtain much better results and with a better user experience

B Analytics

Analytic-1 The top 40 technologies across all job postingsare shown in Fig 5a The top 40 skills that companies in therestaurant chain industry are looking for are shown in Fig5b The top 40 skills that companies in the staffing servicesindustry are looking for are shown in Fig 5c Top 40 skillsthat companies in the health services industry are looking forare shown in Fig 5d

Analytic-2 Top companies that use ldquoTableaurdquo and ldquoMon-goDBrdquo technologies are shown in Fig 6a and top companieswhich hire people with ldquomaster degreerdquo and have ldquoJavardquoprogramming skills are shown in Fig 6b

V CONCLUSIONS AND FUTURE WORK

In this paper we have discussed a methodology that en-hances the job searching experience of users by adding skillset and company attribute filters Job seekers can pinpointjobs by filtering on relevant skill sets technologies numberof employees micro industries and many other attributes Thefiltered jobs are ranked using a relevance score derived froma weighted combination of skill sets and external factors ofcompanies as described in III-F We have also developedadvanced analytics on the data as shown in IV-B

We can additionally enhance the analytics with a temporalaspect by analyzing collected jobs information over time andidentifying trends in the skill set requirements for differentindustries We can also add more filters such as additionalsocial attributes that identify companiesrsquo social presence on

Fig 4 Top 40 Companies with Contacts

(a) Top 40 Technologies(b) Top 40 Skills in theRestaurant Chain Industry

(c) Top 40 Skills in theStaffing Services Industry

(d) Top 40 Skills in theHealth Services Industry

Fig 5 Analytic -1

(a) Top companies that usesldquoTableaurdquo and ldquoMongoDBrdquo

(b) Top recruiters that requirea person who knows ldquojavardquoand has a ldquomaster degreerdquo

Fig 6 Analytic - 2

the web We can also add higher level attributes that arederived from a combination of existing attributes For examplea higher level attribute such as ldquoMarketing Sophisticationrdquo canbe derived by a combination of social presence and advertisingtechnologies attributes

ACKNOWLEDGMENT

We would like to thank Everstring for generously providingthe datasets needed for our research and assisting us throughout this project and being a source of motivation and knowl-edge We would also like to thank Everstringrsquos HPC (HighPerformance Computing) Group for providing us with constanttechnical support over their Hadoop cluster

REFERENCES

[1] Joachims and Thorsten A Probabilistic Analysis of the Rocchio Al-gorithm with TF-IDF for Text Categorization No CMU-CS-96-118Carnegie-Mellon Univ Pittsburgh Pa Dept of Computer Science 1996

[2] W T Yih and C Meek ldquoImproving similarity measures for short seg-ments of textrdquo in Proceedings of the National Conference on ArtificialIntelligence vol 22 no 2 Menlo Park CA Cambridge MA LondonAAAI Press MIT Press 1999 2007

[3] K Jasmeen and V Gupta ldquoEffective approaches for extraction ofkeywordsrdquo IJCSI International Journal of Computer Science Issues 762010

[4] K Zhang et al ldquoKeyword extraction using support vector machinerdquoAdvances in Web-Age Information Management pp 85-96 2006

[5] H Bao and D Zhen ldquoAn Extended Keyword Extraction MethodrdquoPhysics Procedia vol 24 pp 1120-1127 2012

[6] Y Yorozu M Hirano K Oka and Y Tagawa ldquoElectron spectroscopystudies on magneto-optical media and plastic substrate interfacerdquo IEEETransl J Magn Japan vol 2 pp 740ndash741 August 1987 [Digests 9thAnnual Conf Magnetics Japan p 301 1982]

[7] Jussi Myllymaki ldquoEffective Web Data Extraction with Standard XMLTechnologiesrdquo

[8] Qiaoling Liu Faizan Javed and Matt Mcnair CompanyDepotldquordquo Em-ployer Name Normalization in the Online Recruitment Industryrdquo

[9] Fernndez and Miriam et al ldquoSemantically enhanced Information Re-trieval an ontology-based approachrdquo Web semantics Science servicesand agents on the worldwide web 94 pp 434-452 2011

[10] K Soner et al ldquoAn ontology-based retrieval system using semanticindexingrdquo Information Systems vol 37 no 4 pp 294-305 2012

[11] J J Feng et al ldquoKeyword extraction based on sequential pattern min-ingrdquo in Proceedings of the Third International Conference on InternetMultimedia Computing and Service ACM 2011

[12] F Liu F F Liu and Y Liu ldquoA supervised framework for keywordextraction from meeting transcriptsrdquo Audio Speech and LanguageProcessing IEEE Transactions on vol19 no 3 pp 538-548 2011

[13] S H Xu S H Yang and F CM Lau ldquoKeyword extraction and headlinegeneration using novel word featuresrdquo in Proc of the Twenty-FourthAAAI Conference on Artificial Intelligence 2010

[14] I Keisuke and N Mc Cracken ldquoAutomated Keyword Extraction ofLearning Materials Using Semantic Relationsrdquo 2010

[15] C W Bruce D Metzler and T Strohman Search engines Informationretrieval in practice Addison-Wesley 2010

[16] V P Schaik and J Ling ldquoAn integrated model of interaction experiencefor information retrieval in a Web-based encyclopaediardquo Interacting withComputers vol 23 no1 PP18-32 2011

[17] L Yu et al ldquoConcepts Similarity Algorithm Based on Google and KLDistancerdquo Jisuanji Gongcheng Computer Engineering vol 37 no 192011

[18] P I Chen S J Lin and Y C Chu ldquoUsing Google latent semanticdistance to extract the most relevant informationrdquo Expert Systems withApplications vol 38 no 6 pp 7349-7358 2011

[19] P I Chen and S J Lin ldquoAutomatic keyword prediction using Googlesimilarity distancerdquo Expert Systems with Applications vol 37 no3pp 1928-1938 2010

[20] X M Qian X H Hua and X S Hou ldquoTag filtering based onsimilar compatible principlerdquo Image Processing (ICIP) 2012 19th IEEEInternational Conference on IEEE 2012

[21] P I Chen and S J Lin ldquoWord AdHoc network using Google coredistance to extract the most relevant informationrdquo Knowledge-BasedSystems vol 24 no 3 pp 393-405 2011

[22] Ioannis Antonellis and Efstratios Gallopoulos ldquoExploring term-document matrices from matrix models in text miningrdquo

[23] Emmanuel Malherbe and Marie-Aude Aufaure ldquoBridge the terminologygap between recruiters and candidates A multilingual skills base builtfrom social media and linked datardquo

[24] Simon Hughes and his team ldquohttpwwwdicecomskillsrdquo[25] Kush R VarshneyJun Wang Aleksandra MojsilovicDongping Fang

and John H Bauer ldquoPredicting and Recommending Skills in the SocialEnterpriserdquo

[26] Wenjun ZhouYun ZhuFaizan Javed ldquoQuantifying skill relevance to jobtitlesrdquo

[27] Nikita SpirinKarrie Karahalios ldquoUnsupervised Approach to GenerateInformative Structured Snippets for Job Search Enginesrdquo

[28] Khalifeh AlJadda Mohammed KorayemCamilo Ortiz Chris RussellDavid Bernal Lamar Payson Scott Brown and Trey Grainger ldquoAug-menting Recommendation Systems Using a Model of Semantically-related Terms Extracted from User Behaviorrdquo

[29] indeedcom rsquoIndeed Job Search engine APIrsquo2017 [Online] Availablehttpsmarketmashapecomindeedindeed [Accessed 1- Jun- 2017]

[30] googlecom rsquoGoogle launches its AI-powered jobs search enginersquo2017 [Online] Available httpstechcrunchcom20170620google-launches-its-ai-powered-jobs-search-engine [Accessed 1- Aug- 2017]

[31] everstringcom rsquoEverstring APIrsquo 2017 [Online] Availablehttpsapieverstringcomv1companiesdiscover [Accessed 1- Jun-2017]

[32] facebookcom rsquoFacebook job Search Pagersquo 2017[Online] Availablehttpstechcrunchcom20161107jobbook [Accessed 15- July-2017]

[33] alexacom rsquoFind Website Traffic Statistics and Analyticsrsquo Availablehttpwwwalexacomsiteinfo[Accessed 1- Jun- 2017]

[34] Time Tech rsquoGoogle Wants to Help You Search for a New Jobrsquo2017 [Online] Available httptimecom4824968google-job-search[Accessed 20- Jun- 2017]

[35] Example Job pagehttpswwwindeedcomrcclkjk=d93199bf4c06f3bampfccid=d85b448de778e20c

of times in shorter documents should be considered morerelevant than in longer documents This weighting schemaalso incorporates sub-linear term frequency scaling Finallythe scaled TF-IDF is generated for each document by scalingthe maximum TF-IDF value of a skill to 1

E Weights Generation

Ranking the skills using just the TF-IDF score did notproduce good results as we have only taken the job descrip-tions into consideration for generating this score While thejob description contains an extensive amount of informationabout the company the benefits they provide is mixed Theprimary concern is much of the information acts as a noisein our system This drives us to include one more additionaldimensions for a better ranking In order to achieve this weuse the job title to generate a weighting schema on top ofthe TF-IDF that we have computed previously Intuitively wewant a skill to be weighted higher if it occurs more frequentlyunder a title Ngram If a particular skill is very common inthe dataset then it should be weighted lower For example jobpostings with ldquoSoftwarerdquo in the title will weigh the skills likeldquoCrdquoldquoC++rdquoldquoJavardquoldquoOOPrdquo higher as they frequently occur inthe job posting with ldquoSoftwarerdquo in their title They will notoccur in many non technical job postings (a high percentageof job postings in our corpus) which boosts the probabilityof having these skills given ldquoSoftwarerdquo in the title In thisway we have added one more dimension to weigh the skillsby considering the titles of the jobs The weighting schema ismathematically explained using a formula which is explainedin the following sections

1) Title Ngram Generation Initially we normalize the titleand remove all punctuations present in the title We then splitthe title to generate a set of all unigrams present in the titlesThis set is filtered based on the occurrence frequency and stopwords We then generate bigrams from the title by looking atthe sequence of two words in the original normalized title andfiltering out the bigrams by checking if it is present in theoriginal normalized title Eg If we have ldquoBig Data SoftwareEngineerrdquo as a title we look for all bigram sequences (ldquoBigDatardquoldquoData Softwarerdquo and ldquoSoftware Engineerrdquo) in the titleand add the bigrams that are present in the title to generate thefinal list of title Ngrams In this example ldquoSoftware Engineerrdquowill be added to the title Ngrams list After generating this listwe split the normalized title to obtain the unigrams bigramsand filter out the grams that are not present in the title toobtain the title Ngram list for each job title

2) Weight Matrix Generation Now that we have an arrayof title Ngrams for every title in the corpus we use thisinformation to rank the skills that are extracted for eachjob posting (ldquo BOW Generationrdquo in the section above) bygenerating a weight for every skill present in a job postingusing a count matrix between the skills and the title Ngramsacross all the documents The count matrix [22] is computedby generating the term frequency of a particular skill presentin all job descriptions that has a particular title Ngram in itstitle Eg we obtain a count array (of length equal to number

of unique skills) for every unique title Ngram This generatesa matrix giving the count of the occurrences of all skillscorresponding to a particular title Ngram Using this countmatrix we compute the weight for every skill in a job postingby adopting the following formula

weight(skill) =prob(skill|titleNgram)

prob(skill)

The above weighting formula supports our intuition that theskill which is more common across all the documents willbe weighted lower and the skill is weighted higher if theprobability of finding it in all the job descriptions which has aparticular title Ngram is higher For a particular skill in a jobposting we obtain a different weight for each title Ngram ofa job posting because each title in a job posting has multipletitle Ngrams To generate a single weight for each skill in ajob posting we average all of the weights computed for thisskill and all of the job title Ngrams

3) Final Score Generation In the previous section wecomputed the weights for all of the skills in a job postingThis weight is multiplied with the TF-IDFLTU computedpreviously to obtain a score for a particular skill in a jobposting This score and many other external factors about thecompany are used to rank the skills corresponding to a jobposting

F Query Results Ranking

After processing the query the search results are rankedbased on the following formula

Rank(document) = AttributeScore lowastAvg(weight(skill))

Avg(weight(skill)) is the average of all the weights III-E of theskills corresponding to a job posting As we have a weightfor all the skills for each job posting we take the skills givenby the user and compute the aggregated score which is theaverage of user given skills weight

AttributeScore = feedback lowast af lowast ef lowast nlf lowast cks

where af is the alexa Factor [33] ef is the Employee Factor(Afactor computed using the information of the number ofemployees in the company) nlf is the Number of LemmasFactor cks = 2

radicscore where score is the micro industry

keyword score and feedback is the score defined by the numberof user clicks

IV RESULTS

In this section we will show an example of how a jobseeker can search for relevant jobs based on the personrsquosskill set the desired industry space the preferred technologystack and many other skill and company attributes The jobseeker can then query for relevant contacts within the companywhich helps the job seeker to connect and network We thencompared our search results with other job search websitessuch as Indeed and Google

In addition this section presents examples of enhancedanalytics including analysis of the most common skill sets

required by ldquostaffing servicesrdquoldquohealth servicesrdquo and ldquorestau-rant chainrdquo industries the most desired technologies in the USjob market and presenting the top recruiting companies thatare looking for prospects with specific skill sets (ldquoJavardquo andldquoMasters Degreerdquo) or uses specific technologies (ldquoTableaurdquoand ldquoMongoDBrdquo)

A Job Search Results

Query-1 A User with a bachelorrsquos degree and has Pythonand Scala programming skills wants to search for the jobs incompanies which uses jQuery technology wants to work inthe ldquoengineeringrdquo vertical with companies whose revenue isgreat than 1 Million USD (the revenue that we show in thefollowing results are in terms of 1000 USD ) and the numberof employees to be between 50 and 200

The top 40 companies that we identified with the abovesearch query are shown in Fig 2

Fig 2 Top 40 Companies Found Using Query-1

Showing detailed job results for the top three companiesfrom Fig 2 in ranked order is shown in Fig 3 To clarifyon few results which may look odd we have results likeldquoSenior Full stack Java Developerrdquo shown up for the abovequery When looked into the job description [35] (Job Keyd93199bf4c06f3b4) we have the following statement ldquoStrongprofessional experience with Java and at least one otherlanguage - Python Go Scala etcrdquo So this supports our searchquery Here we can also show the ranked job postings basedon the query but here we want to present the results based onthe company similar to topic search engine

The contact information of the recruiters of the top 40companies in the Fig 2 are shown in Fig 4 Consideringthe privacy violation we are not showing the phone numbersand the emails of the recruiters in this paper

No job search website supports querying with number ofemployees and revenue When we searched for a combinationof the five keywords (ldquojQueryrdquo ldquoengineeringrdquo ldquoPythonrdquoldquoScalardquo ldquobachelor degreerdquo) described above we received

Fig 3 Job Details for Top Three Companies

zero results using Googlersquos job search Indeedrsquos job searchproduced many results satisfying any one of the keywords iejobs that satisfy either ldquojQueryrdquo or ldquoengineeringrdquo or ldquoPythonrdquoor ldquoScalardquo or ldquobachelor degreerdquo Our results are not exhaustiveas we ran this experiment on a limited set of job postingsHowever when this approach is used on a larger dataset weobtain much better results and with a better user experience

B Analytics

Analytic-1 The top 40 technologies across all job postingsare shown in Fig 5a The top 40 skills that companies in therestaurant chain industry are looking for are shown in Fig5b The top 40 skills that companies in the staffing servicesindustry are looking for are shown in Fig 5c Top 40 skillsthat companies in the health services industry are looking forare shown in Fig 5d

Analytic-2 Top companies that use ldquoTableaurdquo and ldquoMon-goDBrdquo technologies are shown in Fig 6a and top companieswhich hire people with ldquomaster degreerdquo and have ldquoJavardquoprogramming skills are shown in Fig 6b

V CONCLUSIONS AND FUTURE WORK

In this paper we have discussed a methodology that en-hances the job searching experience of users by adding skillset and company attribute filters Job seekers can pinpointjobs by filtering on relevant skill sets technologies numberof employees micro industries and many other attributes Thefiltered jobs are ranked using a relevance score derived froma weighted combination of skill sets and external factors ofcompanies as described in III-F We have also developedadvanced analytics on the data as shown in IV-B

We can additionally enhance the analytics with a temporalaspect by analyzing collected jobs information over time andidentifying trends in the skill set requirements for differentindustries We can also add more filters such as additionalsocial attributes that identify companiesrsquo social presence on

Fig 4 Top 40 Companies with Contacts

(a) Top 40 Technologies(b) Top 40 Skills in theRestaurant Chain Industry

(c) Top 40 Skills in theStaffing Services Industry

(d) Top 40 Skills in theHealth Services Industry

Fig 5 Analytic -1

(a) Top companies that usesldquoTableaurdquo and ldquoMongoDBrdquo

(b) Top recruiters that requirea person who knows ldquojavardquoand has a ldquomaster degreerdquo

Fig 6 Analytic - 2

the web We can also add higher level attributes that arederived from a combination of existing attributes For examplea higher level attribute such as ldquoMarketing Sophisticationrdquo canbe derived by a combination of social presence and advertisingtechnologies attributes

ACKNOWLEDGMENT

We would like to thank Everstring for generously providingthe datasets needed for our research and assisting us throughout this project and being a source of motivation and knowl-edge We would also like to thank Everstringrsquos HPC (HighPerformance Computing) Group for providing us with constanttechnical support over their Hadoop cluster

REFERENCES

[1] Joachims and Thorsten A Probabilistic Analysis of the Rocchio Al-gorithm with TF-IDF for Text Categorization No CMU-CS-96-118Carnegie-Mellon Univ Pittsburgh Pa Dept of Computer Science 1996

[2] W T Yih and C Meek ldquoImproving similarity measures for short seg-ments of textrdquo in Proceedings of the National Conference on ArtificialIntelligence vol 22 no 2 Menlo Park CA Cambridge MA LondonAAAI Press MIT Press 1999 2007

[3] K Jasmeen and V Gupta ldquoEffective approaches for extraction ofkeywordsrdquo IJCSI International Journal of Computer Science Issues 762010

[4] K Zhang et al ldquoKeyword extraction using support vector machinerdquoAdvances in Web-Age Information Management pp 85-96 2006

[5] H Bao and D Zhen ldquoAn Extended Keyword Extraction MethodrdquoPhysics Procedia vol 24 pp 1120-1127 2012

[6] Y Yorozu M Hirano K Oka and Y Tagawa ldquoElectron spectroscopystudies on magneto-optical media and plastic substrate interfacerdquo IEEETransl J Magn Japan vol 2 pp 740ndash741 August 1987 [Digests 9thAnnual Conf Magnetics Japan p 301 1982]

[7] Jussi Myllymaki ldquoEffective Web Data Extraction with Standard XMLTechnologiesrdquo

[8] Qiaoling Liu Faizan Javed and Matt Mcnair CompanyDepotldquordquo Em-ployer Name Normalization in the Online Recruitment Industryrdquo

[9] Fernndez and Miriam et al ldquoSemantically enhanced Information Re-trieval an ontology-based approachrdquo Web semantics Science servicesand agents on the worldwide web 94 pp 434-452 2011

[10] K Soner et al ldquoAn ontology-based retrieval system using semanticindexingrdquo Information Systems vol 37 no 4 pp 294-305 2012

[11] J J Feng et al ldquoKeyword extraction based on sequential pattern min-ingrdquo in Proceedings of the Third International Conference on InternetMultimedia Computing and Service ACM 2011

[12] F Liu F F Liu and Y Liu ldquoA supervised framework for keywordextraction from meeting transcriptsrdquo Audio Speech and LanguageProcessing IEEE Transactions on vol19 no 3 pp 538-548 2011

[13] S H Xu S H Yang and F CM Lau ldquoKeyword extraction and headlinegeneration using novel word featuresrdquo in Proc of the Twenty-FourthAAAI Conference on Artificial Intelligence 2010

[14] I Keisuke and N Mc Cracken ldquoAutomated Keyword Extraction ofLearning Materials Using Semantic Relationsrdquo 2010

[15] C W Bruce D Metzler and T Strohman Search engines Informationretrieval in practice Addison-Wesley 2010

[16] V P Schaik and J Ling ldquoAn integrated model of interaction experiencefor information retrieval in a Web-based encyclopaediardquo Interacting withComputers vol 23 no1 PP18-32 2011

[17] L Yu et al ldquoConcepts Similarity Algorithm Based on Google and KLDistancerdquo Jisuanji Gongcheng Computer Engineering vol 37 no 192011

[18] P I Chen S J Lin and Y C Chu ldquoUsing Google latent semanticdistance to extract the most relevant informationrdquo Expert Systems withApplications vol 38 no 6 pp 7349-7358 2011

[19] P I Chen and S J Lin ldquoAutomatic keyword prediction using Googlesimilarity distancerdquo Expert Systems with Applications vol 37 no3pp 1928-1938 2010

[20] X M Qian X H Hua and X S Hou ldquoTag filtering based onsimilar compatible principlerdquo Image Processing (ICIP) 2012 19th IEEEInternational Conference on IEEE 2012

[21] P I Chen and S J Lin ldquoWord AdHoc network using Google coredistance to extract the most relevant informationrdquo Knowledge-BasedSystems vol 24 no 3 pp 393-405 2011

[22] Ioannis Antonellis and Efstratios Gallopoulos ldquoExploring term-document matrices from matrix models in text miningrdquo

[23] Emmanuel Malherbe and Marie-Aude Aufaure ldquoBridge the terminologygap between recruiters and candidates A multilingual skills base builtfrom social media and linked datardquo

[24] Simon Hughes and his team ldquohttpwwwdicecomskillsrdquo[25] Kush R VarshneyJun Wang Aleksandra MojsilovicDongping Fang

and John H Bauer ldquoPredicting and Recommending Skills in the SocialEnterpriserdquo

[26] Wenjun ZhouYun ZhuFaizan Javed ldquoQuantifying skill relevance to jobtitlesrdquo

[27] Nikita SpirinKarrie Karahalios ldquoUnsupervised Approach to GenerateInformative Structured Snippets for Job Search Enginesrdquo

[28] Khalifeh AlJadda Mohammed KorayemCamilo Ortiz Chris RussellDavid Bernal Lamar Payson Scott Brown and Trey Grainger ldquoAug-menting Recommendation Systems Using a Model of Semantically-related Terms Extracted from User Behaviorrdquo

[29] indeedcom rsquoIndeed Job Search engine APIrsquo2017 [Online] Availablehttpsmarketmashapecomindeedindeed [Accessed 1- Jun- 2017]

[30] googlecom rsquoGoogle launches its AI-powered jobs search enginersquo2017 [Online] Available httpstechcrunchcom20170620google-launches-its-ai-powered-jobs-search-engine [Accessed 1- Aug- 2017]

[31] everstringcom rsquoEverstring APIrsquo 2017 [Online] Availablehttpsapieverstringcomv1companiesdiscover [Accessed 1- Jun-2017]

[32] facebookcom rsquoFacebook job Search Pagersquo 2017[Online] Availablehttpstechcrunchcom20161107jobbook [Accessed 15- July-2017]

[33] alexacom rsquoFind Website Traffic Statistics and Analyticsrsquo Availablehttpwwwalexacomsiteinfo[Accessed 1- Jun- 2017]

[34] Time Tech rsquoGoogle Wants to Help You Search for a New Jobrsquo2017 [Online] Available httptimecom4824968google-job-search[Accessed 20- Jun- 2017]

[35] Example Job pagehttpswwwindeedcomrcclkjk=d93199bf4c06f3bampfccid=d85b448de778e20c

required by ldquostaffing servicesrdquoldquohealth servicesrdquo and ldquorestau-rant chainrdquo industries the most desired technologies in the USjob market and presenting the top recruiting companies thatare looking for prospects with specific skill sets (ldquoJavardquo andldquoMasters Degreerdquo) or uses specific technologies (ldquoTableaurdquoand ldquoMongoDBrdquo)

A Job Search Results

Query-1 A User with a bachelorrsquos degree and has Pythonand Scala programming skills wants to search for the jobs incompanies which uses jQuery technology wants to work inthe ldquoengineeringrdquo vertical with companies whose revenue isgreat than 1 Million USD (the revenue that we show in thefollowing results are in terms of 1000 USD ) and the numberof employees to be between 50 and 200

The top 40 companies that we identified with the abovesearch query are shown in Fig 2

Fig 2 Top 40 Companies Found Using Query-1

Showing detailed job results for the top three companiesfrom Fig 2 in ranked order is shown in Fig 3 To clarifyon few results which may look odd we have results likeldquoSenior Full stack Java Developerrdquo shown up for the abovequery When looked into the job description [35] (Job Keyd93199bf4c06f3b4) we have the following statement ldquoStrongprofessional experience with Java and at least one otherlanguage - Python Go Scala etcrdquo So this supports our searchquery Here we can also show the ranked job postings basedon the query but here we want to present the results based onthe company similar to topic search engine

The contact information of the recruiters of the top 40companies in the Fig 2 are shown in Fig 4 Consideringthe privacy violation we are not showing the phone numbersand the emails of the recruiters in this paper

No job search website supports querying with number ofemployees and revenue When we searched for a combinationof the five keywords (ldquojQueryrdquo ldquoengineeringrdquo ldquoPythonrdquoldquoScalardquo ldquobachelor degreerdquo) described above we received

Fig 3 Job Details for Top Three Companies

zero results using Googlersquos job search Indeedrsquos job searchproduced many results satisfying any one of the keywords iejobs that satisfy either ldquojQueryrdquo or ldquoengineeringrdquo or ldquoPythonrdquoor ldquoScalardquo or ldquobachelor degreerdquo Our results are not exhaustiveas we ran this experiment on a limited set of job postingsHowever when this approach is used on a larger dataset weobtain much better results and with a better user experience

B Analytics

Analytic-1 The top 40 technologies across all job postingsare shown in Fig 5a The top 40 skills that companies in therestaurant chain industry are looking for are shown in Fig5b The top 40 skills that companies in the staffing servicesindustry are looking for are shown in Fig 5c Top 40 skillsthat companies in the health services industry are looking forare shown in Fig 5d

Analytic-2 Top companies that use ldquoTableaurdquo and ldquoMon-goDBrdquo technologies are shown in Fig 6a and top companieswhich hire people with ldquomaster degreerdquo and have ldquoJavardquoprogramming skills are shown in Fig 6b

V CONCLUSIONS AND FUTURE WORK

In this paper we have discussed a methodology that en-hances the job searching experience of users by adding skillset and company attribute filters Job seekers can pinpointjobs by filtering on relevant skill sets technologies numberof employees micro industries and many other attributes Thefiltered jobs are ranked using a relevance score derived froma weighted combination of skill sets and external factors ofcompanies as described in III-F We have also developedadvanced analytics on the data as shown in IV-B

We can additionally enhance the analytics with a temporalaspect by analyzing collected jobs information over time andidentifying trends in the skill set requirements for differentindustries We can also add more filters such as additionalsocial attributes that identify companiesrsquo social presence on

Fig 4 Top 40 Companies with Contacts

(a) Top 40 Technologies(b) Top 40 Skills in theRestaurant Chain Industry

(c) Top 40 Skills in theStaffing Services Industry

(d) Top 40 Skills in theHealth Services Industry

Fig 5 Analytic -1

(a) Top companies that usesldquoTableaurdquo and ldquoMongoDBrdquo

(b) Top recruiters that requirea person who knows ldquojavardquoand has a ldquomaster degreerdquo

Fig 6 Analytic - 2

the web We can also add higher level attributes that arederived from a combination of existing attributes For examplea higher level attribute such as ldquoMarketing Sophisticationrdquo canbe derived by a combination of social presence and advertisingtechnologies attributes

ACKNOWLEDGMENT

We would like to thank Everstring for generously providingthe datasets needed for our research and assisting us throughout this project and being a source of motivation and knowl-edge We would also like to thank Everstringrsquos HPC (HighPerformance Computing) Group for providing us with constanttechnical support over their Hadoop cluster

REFERENCES

[1] Joachims and Thorsten A Probabilistic Analysis of the Rocchio Al-gorithm with TF-IDF for Text Categorization No CMU-CS-96-118Carnegie-Mellon Univ Pittsburgh Pa Dept of Computer Science 1996

[2] W T Yih and C Meek ldquoImproving similarity measures for short seg-ments of textrdquo in Proceedings of the National Conference on ArtificialIntelligence vol 22 no 2 Menlo Park CA Cambridge MA LondonAAAI Press MIT Press 1999 2007

[3] K Jasmeen and V Gupta ldquoEffective approaches for extraction ofkeywordsrdquo IJCSI International Journal of Computer Science Issues 762010

[4] K Zhang et al ldquoKeyword extraction using support vector machinerdquoAdvances in Web-Age Information Management pp 85-96 2006

[5] H Bao and D Zhen ldquoAn Extended Keyword Extraction MethodrdquoPhysics Procedia vol 24 pp 1120-1127 2012

[6] Y Yorozu M Hirano K Oka and Y Tagawa ldquoElectron spectroscopystudies on magneto-optical media and plastic substrate interfacerdquo IEEETransl J Magn Japan vol 2 pp 740ndash741 August 1987 [Digests 9thAnnual Conf Magnetics Japan p 301 1982]

[7] Jussi Myllymaki ldquoEffective Web Data Extraction with Standard XMLTechnologiesrdquo

[8] Qiaoling Liu Faizan Javed and Matt Mcnair CompanyDepotldquordquo Em-ployer Name Normalization in the Online Recruitment Industryrdquo

[9] Fernndez and Miriam et al ldquoSemantically enhanced Information Re-trieval an ontology-based approachrdquo Web semantics Science servicesand agents on the worldwide web 94 pp 434-452 2011

[10] K Soner et al ldquoAn ontology-based retrieval system using semanticindexingrdquo Information Systems vol 37 no 4 pp 294-305 2012

[11] J J Feng et al ldquoKeyword extraction based on sequential pattern min-ingrdquo in Proceedings of the Third International Conference on InternetMultimedia Computing and Service ACM 2011

[12] F Liu F F Liu and Y Liu ldquoA supervised framework for keywordextraction from meeting transcriptsrdquo Audio Speech and LanguageProcessing IEEE Transactions on vol19 no 3 pp 538-548 2011

[13] S H Xu S H Yang and F CM Lau ldquoKeyword extraction and headlinegeneration using novel word featuresrdquo in Proc of the Twenty-FourthAAAI Conference on Artificial Intelligence 2010

[14] I Keisuke and N Mc Cracken ldquoAutomated Keyword Extraction ofLearning Materials Using Semantic Relationsrdquo 2010

[15] C W Bruce D Metzler and T Strohman Search engines Informationretrieval in practice Addison-Wesley 2010

[16] V P Schaik and J Ling ldquoAn integrated model of interaction experiencefor information retrieval in a Web-based encyclopaediardquo Interacting withComputers vol 23 no1 PP18-32 2011

[17] L Yu et al ldquoConcepts Similarity Algorithm Based on Google and KLDistancerdquo Jisuanji Gongcheng Computer Engineering vol 37 no 192011

[18] P I Chen S J Lin and Y C Chu ldquoUsing Google latent semanticdistance to extract the most relevant informationrdquo Expert Systems withApplications vol 38 no 6 pp 7349-7358 2011

[19] P I Chen and S J Lin ldquoAutomatic keyword prediction using Googlesimilarity distancerdquo Expert Systems with Applications vol 37 no3pp 1928-1938 2010

[20] X M Qian X H Hua and X S Hou ldquoTag filtering based onsimilar compatible principlerdquo Image Processing (ICIP) 2012 19th IEEEInternational Conference on IEEE 2012

[21] P I Chen and S J Lin ldquoWord AdHoc network using Google coredistance to extract the most relevant informationrdquo Knowledge-BasedSystems vol 24 no 3 pp 393-405 2011

[22] Ioannis Antonellis and Efstratios Gallopoulos ldquoExploring term-document matrices from matrix models in text miningrdquo

[23] Emmanuel Malherbe and Marie-Aude Aufaure ldquoBridge the terminologygap between recruiters and candidates A multilingual skills base builtfrom social media and linked datardquo

[24] Simon Hughes and his team ldquohttpwwwdicecomskillsrdquo[25] Kush R VarshneyJun Wang Aleksandra MojsilovicDongping Fang

and John H Bauer ldquoPredicting and Recommending Skills in the SocialEnterpriserdquo

[26] Wenjun ZhouYun ZhuFaizan Javed ldquoQuantifying skill relevance to jobtitlesrdquo

[27] Nikita SpirinKarrie Karahalios ldquoUnsupervised Approach to GenerateInformative Structured Snippets for Job Search Enginesrdquo

[28] Khalifeh AlJadda Mohammed KorayemCamilo Ortiz Chris RussellDavid Bernal Lamar Payson Scott Brown and Trey Grainger ldquoAug-menting Recommendation Systems Using a Model of Semantically-related Terms Extracted from User Behaviorrdquo

[29] indeedcom rsquoIndeed Job Search engine APIrsquo2017 [Online] Availablehttpsmarketmashapecomindeedindeed [Accessed 1- Jun- 2017]

[30] googlecom rsquoGoogle launches its AI-powered jobs search enginersquo2017 [Online] Available httpstechcrunchcom20170620google-launches-its-ai-powered-jobs-search-engine [Accessed 1- Aug- 2017]

[31] everstringcom rsquoEverstring APIrsquo 2017 [Online] Availablehttpsapieverstringcomv1companiesdiscover [Accessed 1- Jun-2017]

[32] facebookcom rsquoFacebook job Search Pagersquo 2017[Online] Availablehttpstechcrunchcom20161107jobbook [Accessed 15- July-2017]

[33] alexacom rsquoFind Website Traffic Statistics and Analyticsrsquo Availablehttpwwwalexacomsiteinfo[Accessed 1- Jun- 2017]

[34] Time Tech rsquoGoogle Wants to Help You Search for a New Jobrsquo2017 [Online] Available httptimecom4824968google-job-search[Accessed 20- Jun- 2017]

[35] Example Job pagehttpswwwindeedcomrcclkjk=d93199bf4c06f3bampfccid=d85b448de778e20c

Fig 4 Top 40 Companies with Contacts

(a) Top 40 Technologies(b) Top 40 Skills in theRestaurant Chain Industry

(c) Top 40 Skills in theStaffing Services Industry

(d) Top 40 Skills in theHealth Services Industry

Fig 5 Analytic -1

(a) Top companies that usesldquoTableaurdquo and ldquoMongoDBrdquo

(b) Top recruiters that requirea person who knows ldquojavardquoand has a ldquomaster degreerdquo

Fig 6 Analytic - 2

the web We can also add higher level attributes that arederived from a combination of existing attributes For examplea higher level attribute such as ldquoMarketing Sophisticationrdquo canbe derived by a combination of social presence and advertisingtechnologies attributes

ACKNOWLEDGMENT

We would like to thank Everstring for generously providingthe datasets needed for our research and assisting us throughout this project and being a source of motivation and knowl-edge We would also like to thank Everstringrsquos HPC (HighPerformance Computing) Group for providing us with constanttechnical support over their Hadoop cluster

REFERENCES

[1] Joachims and Thorsten A Probabilistic Analysis of the Rocchio Al-gorithm with TF-IDF for Text Categorization No CMU-CS-96-118Carnegie-Mellon Univ Pittsburgh Pa Dept of Computer Science 1996

[2] W T Yih and C Meek ldquoImproving similarity measures for short seg-ments of textrdquo in Proceedings of the National Conference on ArtificialIntelligence vol 22 no 2 Menlo Park CA Cambridge MA LondonAAAI Press MIT Press 1999 2007

[3] K Jasmeen and V Gupta ldquoEffective approaches for extraction ofkeywordsrdquo IJCSI International Journal of Computer Science Issues 762010

[4] K Zhang et al ldquoKeyword extraction using support vector machinerdquoAdvances in Web-Age Information Management pp 85-96 2006

[5] H Bao and D Zhen ldquoAn Extended Keyword Extraction MethodrdquoPhysics Procedia vol 24 pp 1120-1127 2012

[6] Y Yorozu M Hirano K Oka and Y Tagawa ldquoElectron spectroscopystudies on magneto-optical media and plastic substrate interfacerdquo IEEETransl J Magn Japan vol 2 pp 740ndash741 August 1987 [Digests 9thAnnual Conf Magnetics Japan p 301 1982]

[7] Jussi Myllymaki ldquoEffective Web Data Extraction with Standard XMLTechnologiesrdquo

[8] Qiaoling Liu Faizan Javed and Matt Mcnair CompanyDepotldquordquo Em-ployer Name Normalization in the Online Recruitment Industryrdquo

[9] Fernndez and Miriam et al ldquoSemantically enhanced Information Re-trieval an ontology-based approachrdquo Web semantics Science servicesand agents on the worldwide web 94 pp 434-452 2011

[10] K Soner et al ldquoAn ontology-based retrieval system using semanticindexingrdquo Information Systems vol 37 no 4 pp 294-305 2012

[11] J J Feng et al ldquoKeyword extraction based on sequential pattern min-ingrdquo in Proceedings of the Third International Conference on InternetMultimedia Computing and Service ACM 2011

[12] F Liu F F Liu and Y Liu ldquoA supervised framework for keywordextraction from meeting transcriptsrdquo Audio Speech and LanguageProcessing IEEE Transactions on vol19 no 3 pp 538-548 2011

[13] S H Xu S H Yang and F CM Lau ldquoKeyword extraction and headlinegeneration using novel word featuresrdquo in Proc of the Twenty-FourthAAAI Conference on Artificial Intelligence 2010

[14] I Keisuke and N Mc Cracken ldquoAutomated Keyword Extraction ofLearning Materials Using Semantic Relationsrdquo 2010

[15] C W Bruce D Metzler and T Strohman Search engines Informationretrieval in practice Addison-Wesley 2010

[16] V P Schaik and J Ling ldquoAn integrated model of interaction experiencefor information retrieval in a Web-based encyclopaediardquo Interacting withComputers vol 23 no1 PP18-32 2011

[17] L Yu et al ldquoConcepts Similarity Algorithm Based on Google and KLDistancerdquo Jisuanji Gongcheng Computer Engineering vol 37 no 192011

[18] P I Chen S J Lin and Y C Chu ldquoUsing Google latent semanticdistance to extract the most relevant informationrdquo Expert Systems withApplications vol 38 no 6 pp 7349-7358 2011

[19] P I Chen and S J Lin ldquoAutomatic keyword prediction using Googlesimilarity distancerdquo Expert Systems with Applications vol 37 no3pp 1928-1938 2010

[20] X M Qian X H Hua and X S Hou ldquoTag filtering based onsimilar compatible principlerdquo Image Processing (ICIP) 2012 19th IEEEInternational Conference on IEEE 2012

[21] P I Chen and S J Lin ldquoWord AdHoc network using Google coredistance to extract the most relevant informationrdquo Knowledge-BasedSystems vol 24 no 3 pp 393-405 2011

[22] Ioannis Antonellis and Efstratios Gallopoulos ldquoExploring term-document matrices from matrix models in text miningrdquo

[23] Emmanuel Malherbe and Marie-Aude Aufaure ldquoBridge the terminologygap between recruiters and candidates A multilingual skills base builtfrom social media and linked datardquo

[24] Simon Hughes and his team ldquohttpwwwdicecomskillsrdquo[25] Kush R VarshneyJun Wang Aleksandra MojsilovicDongping Fang

and John H Bauer ldquoPredicting and Recommending Skills in the SocialEnterpriserdquo

[26] Wenjun ZhouYun ZhuFaizan Javed ldquoQuantifying skill relevance to jobtitlesrdquo

[27] Nikita SpirinKarrie Karahalios ldquoUnsupervised Approach to GenerateInformative Structured Snippets for Job Search Enginesrdquo

[28] Khalifeh AlJadda Mohammed KorayemCamilo Ortiz Chris RussellDavid Bernal Lamar Payson Scott Brown and Trey Grainger ldquoAug-menting Recommendation Systems Using a Model of Semantically-related Terms Extracted from User Behaviorrdquo

[29] indeedcom rsquoIndeed Job Search engine APIrsquo2017 [Online] Availablehttpsmarketmashapecomindeedindeed [Accessed 1- Jun- 2017]

[30] googlecom rsquoGoogle launches its AI-powered jobs search enginersquo2017 [Online] Available httpstechcrunchcom20170620google-launches-its-ai-powered-jobs-search-engine [Accessed 1- Aug- 2017]

[31] everstringcom rsquoEverstring APIrsquo 2017 [Online] Availablehttpsapieverstringcomv1companiesdiscover [Accessed 1- Jun-2017]

[32] facebookcom rsquoFacebook job Search Pagersquo 2017[Online] Availablehttpstechcrunchcom20161107jobbook [Accessed 15- July-2017]

[33] alexacom rsquoFind Website Traffic Statistics and Analyticsrsquo Availablehttpwwwalexacomsiteinfo[Accessed 1- Jun- 2017]

[34] Time Tech rsquoGoogle Wants to Help You Search for a New Jobrsquo2017 [Online] Available httptimecom4824968google-job-search[Accessed 20- Jun- 2017]

[35] Example Job pagehttpswwwindeedcomrcclkjk=d93199bf4c06f3bampfccid=d85b448de778e20c

(a) Top companies that usesldquoTableaurdquo and ldquoMongoDBrdquo

(b) Top recruiters that requirea person who knows ldquojavardquoand has a ldquomaster degreerdquo

Fig 6 Analytic - 2

the web We can also add higher level attributes that arederived from a combination of existing attributes For examplea higher level attribute such as ldquoMarketing Sophisticationrdquo canbe derived by a combination of social presence and advertisingtechnologies attributes

ACKNOWLEDGMENT

We would like to thank Everstring for generously providingthe datasets needed for our research and assisting us throughout this project and being a source of motivation and knowl-edge We would also like to thank Everstringrsquos HPC (HighPerformance Computing) Group for providing us with constanttechnical support over their Hadoop cluster

REFERENCES

[1] Joachims and Thorsten A Probabilistic Analysis of the Rocchio Al-gorithm with TF-IDF for Text Categorization No CMU-CS-96-118Carnegie-Mellon Univ Pittsburgh Pa Dept of Computer Science 1996

[2] W T Yih and C Meek ldquoImproving similarity measures for short seg-ments of textrdquo in Proceedings of the National Conference on ArtificialIntelligence vol 22 no 2 Menlo Park CA Cambridge MA LondonAAAI Press MIT Press 1999 2007

[3] K Jasmeen and V Gupta ldquoEffective approaches for extraction ofkeywordsrdquo IJCSI International Journal of Computer Science Issues 762010

[4] K Zhang et al ldquoKeyword extraction using support vector machinerdquoAdvances in Web-Age Information Management pp 85-96 2006

[5] H Bao and D Zhen ldquoAn Extended Keyword Extraction MethodrdquoPhysics Procedia vol 24 pp 1120-1127 2012

[6] Y Yorozu M Hirano K Oka and Y Tagawa ldquoElectron spectroscopystudies on magneto-optical media and plastic substrate interfacerdquo IEEETransl J Magn Japan vol 2 pp 740ndash741 August 1987 [Digests 9thAnnual Conf Magnetics Japan p 301 1982]

[7] Jussi Myllymaki ldquoEffective Web Data Extraction with Standard XMLTechnologiesrdquo

[8] Qiaoling Liu Faizan Javed and Matt Mcnair CompanyDepotldquordquo Em-ployer Name Normalization in the Online Recruitment Industryrdquo

[9] Fernndez and Miriam et al ldquoSemantically enhanced Information Re-trieval an ontology-based approachrdquo Web semantics Science servicesand agents on the worldwide web 94 pp 434-452 2011

[10] K Soner et al ldquoAn ontology-based retrieval system using semanticindexingrdquo Information Systems vol 37 no 4 pp 294-305 2012

[11] J J Feng et al ldquoKeyword extraction based on sequential pattern min-ingrdquo in Proceedings of the Third International Conference on InternetMultimedia Computing and Service ACM 2011

[12] F Liu F F Liu and Y Liu ldquoA supervised framework for keywordextraction from meeting transcriptsrdquo Audio Speech and LanguageProcessing IEEE Transactions on vol19 no 3 pp 538-548 2011

[13] S H Xu S H Yang and F CM Lau ldquoKeyword extraction and headlinegeneration using novel word featuresrdquo in Proc of the Twenty-FourthAAAI Conference on Artificial Intelligence 2010

[14] I Keisuke and N Mc Cracken ldquoAutomated Keyword Extraction ofLearning Materials Using Semantic Relationsrdquo 2010

[15] C W Bruce D Metzler and T Strohman Search engines Informationretrieval in practice Addison-Wesley 2010

[16] V P Schaik and J Ling ldquoAn integrated model of interaction experiencefor information retrieval in a Web-based encyclopaediardquo Interacting withComputers vol 23 no1 PP18-32 2011

[17] L Yu et al ldquoConcepts Similarity Algorithm Based on Google and KLDistancerdquo Jisuanji Gongcheng Computer Engineering vol 37 no 192011

[18] P I Chen S J Lin and Y C Chu ldquoUsing Google latent semanticdistance to extract the most relevant informationrdquo Expert Systems withApplications vol 38 no 6 pp 7349-7358 2011

[19] P I Chen and S J Lin ldquoAutomatic keyword prediction using Googlesimilarity distancerdquo Expert Systems with Applications vol 37 no3pp 1928-1938 2010

[20] X M Qian X H Hua and X S Hou ldquoTag filtering based onsimilar compatible principlerdquo Image Processing (ICIP) 2012 19th IEEEInternational Conference on IEEE 2012

[21] P I Chen and S J Lin ldquoWord AdHoc network using Google coredistance to extract the most relevant informationrdquo Knowledge-BasedSystems vol 24 no 3 pp 393-405 2011

[22] Ioannis Antonellis and Efstratios Gallopoulos ldquoExploring term-document matrices from matrix models in text miningrdquo

[23] Emmanuel Malherbe and Marie-Aude Aufaure ldquoBridge the terminologygap between recruiters and candidates A multilingual skills base builtfrom social media and linked datardquo

[24] Simon Hughes and his team ldquohttpwwwdicecomskillsrdquo[25] Kush R VarshneyJun Wang Aleksandra MojsilovicDongping Fang

and John H Bauer ldquoPredicting and Recommending Skills in the SocialEnterpriserdquo

[26] Wenjun ZhouYun ZhuFaizan Javed ldquoQuantifying skill relevance to jobtitlesrdquo

[27] Nikita SpirinKarrie Karahalios ldquoUnsupervised Approach to GenerateInformative Structured Snippets for Job Search Enginesrdquo

[28] Khalifeh AlJadda Mohammed KorayemCamilo Ortiz Chris RussellDavid Bernal Lamar Payson Scott Brown and Trey Grainger ldquoAug-menting Recommendation Systems Using a Model of Semantically-related Terms Extracted from User Behaviorrdquo

[29] indeedcom rsquoIndeed Job Search engine APIrsquo2017 [Online] Availablehttpsmarketmashapecomindeedindeed [Accessed 1- Jun- 2017]

[30] googlecom rsquoGoogle launches its AI-powered jobs search enginersquo2017 [Online] Available httpstechcrunchcom20170620google-launches-its-ai-powered-jobs-search-engine [Accessed 1- Aug- 2017]

[31] everstringcom rsquoEverstring APIrsquo 2017 [Online] Availablehttpsapieverstringcomv1companiesdiscover [Accessed 1- Jun-2017]

[32] facebookcom rsquoFacebook job Search Pagersquo 2017[Online] Availablehttpstechcrunchcom20161107jobbook [Accessed 15- July-2017]

[33] alexacom rsquoFind Website Traffic Statistics and Analyticsrsquo Availablehttpwwwalexacomsiteinfo[Accessed 1- Jun- 2017]

[34] Time Tech rsquoGoogle Wants to Help You Search for a New Jobrsquo2017 [Online] Available httptimecom4824968google-job-search[Accessed 20- Jun- 2017]

[35] Example Job pagehttpswwwindeedcomrcclkjk=d93199bf4c06f3bampfccid=d85b448de778e20c