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Personalized Keyword Search Related Works ANNE JERONEN, ARMAND NOUBISIE, YUDIT PONCE

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Personalized

Keyword SearchRelated WorksANNE JERONEN, ARMAND NOUBISIE, YUDIT PONCE

Introduction

Georgia Koutrika, Alkis Simitsis, Yannis E. Ioannidis: Précis: The

Essence of a Query Answer. ICDE 2006

Kostas Stefanidis, Marina Drosou, Evaggelia Pitoura: PerK:

Personalized Keyword Search in Relational Databases through

Preferences. EDBT 2010

Personalized Keyword Search

Keyword search

Enables users to find information without knowing the database schema

or complicated queries

May return a huge volume of data

Benefits of personalization

Enables returning personalized results to individual users or groups

Enables returning smaller number of results that are also more relevant

to the user(s)

Personalized Keyword Search

Personalization can be implemented in several ways:

Weights assigned to the edges of the database graphs (Koutrika et al.

2006)

Pairwise preferential ordering (Stefanidis et al. 2010)

Q = {western}:

cp1 = ({western}, C. Eastwood ≻ J. Wayne)

MOVIE GENRE

(MID) 0.9

(MID) 1.0

C.

Eastwood

J. Wayne

Sources

arXiv.org (arxiv.org)

Google Scholar (scholar.google.com)

Google (www.google.com)

Keywords: keyword, keyword-search, keyword-based, personalized,

personalization, database, query, search, user profile, preferential,

preferences, ordering

Current Trends: Overview

Online services

Cloud services

E-Commerce

Current Trend: Online Services

Personalization in search engine results

Personalization through browsing history

Search Personalization using Machine Learning. H. Yoganarasimhan. 2014.

http://faculty.washington.edu/hemay/search_personalization.pdf

The Internet of Things

Geo-location used as an external context to offer personalization

Current Trends: Cloud Services

Ranked keyword search for encrypted data

A Novel Approach for Secure and Dynamic Multi-Keyword Ranked

Search Scheme over Encrypted Cloud Data. V. Guguloth, P. Reddy, V.

Pasunuri. 2016. http://ijitech.org/uploads/362451IJIT10110-245.pdf

Enabling Fine-grained Multi-keyword Search Supporting Classified Sub-

dictionaries over Encrypted Cloud Data. H. Li, Y. Yang, T. Luan, X. Liang,

L. Zhou, X. Shen. 2015.

http://www.ieeeprojectsmadurai.com/Basepapers/enabling%20fine.pdf

Preferred Keyword Search over Encrypted Data in Cloud Computing. Z.

Shen, J. Shu, W. Xue. 2013.

https://www.researchgate.net/profile/Zhirong_Shen2/publication/26115

2238_Preferred_keyword_search_over_encrypted_data_in_cloud_comp

uting/links/555c45c108aec5ac2232afc3.pdf

Current Trends: E-Commerce

Keyword search and personalization of results in e-commerce

product searches

A Modern Approach to Integrate Database Queries for Searching E-

Commerce Product. A. Siddiqui, M. Muntjir. 2016.

https://www.researchgate.net/publication/302393360_A_Modern_Appr

oach_to_Integrate_Database_Queries_for_Searching_E-

Commerce_Product

Supporting Keyword Search in Product Database: A Probabilistic

Approach. H. Duan, C. Zhai, J. Cheng, A. Gattani. 2013.

http://www.vldb.org/pvldb/vol6/p1786-duan.pdf

E-Commerce and Product Search

Growing field

Multiple product categories and a huge volume of data

Keyword search simplifies searches and produces accurate results

nVidia 960m

Computers Accessories Displays

Desktops

Laptops

Accessories

Displays

Cables

Bags

Speakers

Microphones

Other

<13’’ displays

13’’-15’’ displays

>15’’ displays

E-Commerce and Product Search- Duan et al. 2013

Vocabulary gap between the specifications of products in the

database and the keywords people use in search queries (H. Duan et al.

2013).

Probabilistic model to find and rank the most relevant items for generic queries

new computer

asus rog i7 win10 nvidia geforce 960m

laptop with dedicated graphics card

Additional Reading

Thesis: Beyond Keyword Search: Representations and Models for

Personalization. K. El-Arini. 2013.

http://www.cs.cmu.edu/~./kbe/kbe_thesis.pdf

Additional Sources

sciencedirect.com

Leading information solution for researchers, teachers, students, health careprofessionals and information professionals . It is the world's largest electroniccollection of science, technology, and medicine full-text and bibliographicinformation. It offers a wide variety of features and content.

acm.org

The world's largest educational and scientific computing society, deliversresources that advance computing as a science and a profession. ACM providesthe computing field's premier Digital Library and serves its members and thecomputing profession with leading-edge publications, conferences, and careerresources.

Ieeexplore.ieee.org:

Provides web access to more than four-million full-text documents from some ofthe world's most highly cited publications in electrical engineering, computerscience and electronics.

Current Trends: Overview

Social Media ( Twitter, Facebook, LinkedIn, etc)

Items and user context data are highly dynamic: Real-time scoring in personalized keyword search.

Implicit feedback techniques based on user's interactions to automatically capture user’s interests.

Personalized keyword search and semantic approach.

User information needs are typically rather complicated.

Personalized keyword search on XML documents.

Current Trends: Social Media

Real-time scoring in personalized keyword search.

F. Borisyuk, K. Kenthapadi, D. Stain, B. Zhao: "CaSMoS: A Framework for Learning CandidateSelection Models over Structured Queries and Documents " . 2016

Propose a machine learned candidate selection framework to prune irrelevantdocuments.

LinkedIn: Web Platform to find people, jobs, companies, groups and other professional content.

Results in real time, must offer a high degree of data freshness, and must respond with low latency.

Search engines are known to be good at retrieving a small set of relevant documents matching agiven query out of a huge document set.

Retrieval process in two stages: 1.First invoke a computationally inexpensive model to select asmall candidate. 2. Perform a more computationally expensive second pass scoring and ranking onthe obtained candidate set.

Current Trends: Social Media

Real-time scoring in personalized keyword search.

Y. Cheng, Y. Xie, Z. Chen, A. Agrawal, A. Choudhary: "JobMiner: a real-time

system for mining job-related patterns from social media " .Proceedings of the 19th

ACM SIGKDD international conference on Knowledge discovery and data mining,

2013.

LinkedIn: Web Platform to find people, jobs, companies, groups and other

professional content.

JobMiner: providing a better understanding of the dynamic processes in real

time in this kind of platforms and in that way obtaining a more accurate

identification of important entities in the job market.

Current Trends: Implicit feedback

techniques

An explicit request of information to the user implies to burden the user, and

to rely on the user’s willingness to specify the required information.

Sometimes this scenario is not realistic.

Several techniques have been proposed to automatically capture the user’s

interests: They are based on the collection and the analysis of: browser

history, query history, click-trough data, desktop information, document

display time, bookmarks, and several other kinds of signals.

Current Trends: Implicit feedback

techniques

G. Pasi: "Implicit feedback through user-system interactions for defining usermodels in personalized search", 2014.

Implicit feedback techniques to collect user interests and preferences via user-systeminteractions are presented.

k. Gao, X. Dong: "A Context-awareness Based Dynamic Personalized HierarchicalOntology Modeling Approach", 2016.

Developing a dynamic updated ontology model based on capturing the implicitcontext information and implementing the personalized scalability demands of contextmodel for different users.

E. Nunez, J. Cueva, O. Sanjuan, V- Garcia, "Implicit feedback techniqueson recommender systems applied to electronic books", 2012.

Current Trends: Semantic approach

Personalized keyword search and semantic approach.

J.Li, D. Arja,V. Ha-Thuc,S. Sinha: "How to Get Them a Dream Job? Entity-

Aware Features for Personalized Job Search Ranking",2016.

Traditional information retrieval systems, and particularly Web search engines,

have focused on keyword matching.

LinkedIn: Web Platform to find people, jobs, companies, groups and other

professional content.

It is important to understand the structure of the information.

Current Trends: Semantic approach

Personalized keyword search and semantic approach.

M. Tvarožek , M. Bieliková: "Personalized Exploratory Search in the Semantic Web",

2010.

Effective access to information on the Web is being hampered by informationoverload, unavailability of information, navigation issues and user diversity.

Goal: view generation with incremental graph visualization to enable end-

user grade exploration of semantic web content.

Current Trends: Personalized Keyword

Search on XML documents

B. Kurinchi Meenakshi 1 , Dr.C.Nalini, "Managing XML Retrieval throughPersonalization Using Search Engine". International Journal of Innovative Researchin Computer and Communication Engineering, 2015.

Improving the search of XML documents according to the user requirement andpreference.

X. Liu , L. Chen, Ch. Wan, D. Liu, N. Xiong, "Exploiting structures in keyword queriesfor effective XML search ", 2015.

Focused on the query side of XML keyword search.

G. Li, C. Li, A. Li, J. Feng, A. Li, L. Zhou: "SAIL: Structure-aware indexing for effectiveand progressive top-k keyword search over XML documents", 2011.

It develops a novel method, called SAIL, for efficient XML keyword search.

Additional Reading Personalization & Search Engine Rankings. Search Engine Land.

http://searchengineland.com/guide/seo/personalization-search-engine-rankings

Advantages and Disadvantages of Personalized Search, RAJIB KUMAR,

http://www.techncom.net/2012/03/advantages-disadvantages-personalized-

search/

scholar.google.com

Personalized Task Recommendation in Crowsourcing

Current Trends: Personalized search

Personalization & Search Engine Rankings.

Search result was the same years ago # The search result of today are

personalized.

Locality

CountryPersonal History

Social Connections

Country

Someone in the US searching for “football” will get results about American football; someone in the UK will get results about the type of football that Americans would call soccer.

Locality : Search engines tailor results to match the city.

Personal History : Used by SEO (Google, Bing)

What has someone been searching for and clicking on from their search results?

What sites do they regularly visit?

Have they “Liked” a site using Facebook, shared it via Twitter ?

Personalized History therefore takes personalization to an individual level.

Social Connections

It is based on someone’s friends behaviour on Social network i.e.; What does your friends think about

a website ?

Search engines view those connections as a user’s personal set of advisors (As it happens offline).

Elements of Personalization & How to Perform Better in PersonalizedSearch .

In this articles, more detailed used by SE to personalized a search are added.

Location : where is the searcher

Device : What type of devices or OS used

Browser: Search history, potentially what you’ve clicked on in the

results.

Email calendar: In the case you are using Gmail, Google

calendar, Google will use the data for potential search result

Google+

Visit history

Bookmarks :

Logged-in visitors

If we logged-out the search the results of a search will appears differently still they are many

ways of personalization. A fresh and unused window will show a different result than those of

the browser that we use everyday.

Performing better personalized search :

Get potential searchers to know and love your brand before the query

Be visible in all the relevant locations for your business

Get those keyword targets dialed in

Share content on Google+ and connect with your potential customers

Be multi-device friendly and usable

Advantages and Disadvantages of Personalized Search.

The starting point of personalized search was the launch of the

‘Search plus Your World’ program by Google.

The Goal was to enable people with who you interact within

your community could have a direct influence on the search

results shown to you.

As a result nowadays, it is impossible to ignore personalization in

search engine query

Advantages of Personalized Search.

Personalized make search result centered to the users needs and context of use.

It had increase the creation of high quality, interactive content for the benefit of the users .

Disadvantages of Personalized Search.

Personalized restrict the users scope of search and force to operate certain limitations, therefore

limit the users to discover new result outside his circle.

In the case of Google’s “Search Plus Your World”, personalization of the user’s search does not

include other social media sites as Facebook, Twitter.