skills, reputation, and search
DESCRIPTION
This keynote presentation describes the critical role that search and Lucene has in building next generation products that understand reputation and relevance. We also describe how data science and machine learning have been applied at LinkedIn to collect, interpret, and index data around topical reputation. Lucene Revolution is the biggest open source conference dedicated to Apache Lucene/Solr.TRANSCRIPT
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Skills, Reputation, and Search
Pete SkomorochPrincipal Data Scientist, LinkedIn
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Vision: Create Economic Opportunity for Every Professional
TimeLocation
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©2012 LinkedIn Corporation. All Rights Reserved.
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LinkedIn: The Professional Profile of Record
200+MMembers 200M MemberProfiles
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LinkedIn Search: Connecting Talent with Opportunity
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Skills Correlated with the Job Title “Data Scientist”
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Skills Related to “Big Data”
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Information Retrieval
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Soul Retrieval
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Lucene on LinkedIn
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Lucene Endorsement Graph
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Solr on LinkedIn
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Solr Endorsement Graph
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Reputation: Building the Endorsement Graph
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Viral Growth: 1 Billion Endorsements in 5 Months
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How Did We Gather this Data?
1. Desire + Social Proof
2. Viral Loops + Network Effects
3. Data Foundation + Recommendation Algorithms
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1) Desire & Social Proof
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A endorses
B
B notified
B “accepts” endorsement
B endorses
C
B endorses
D
Endorsement recommendations
Email NotificationNews Feed2) Viral Loops & Network Effects
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3) Data Foundation: Skills & Suggested Skills
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Data Foundation: LinkedIn Skills
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Social Tagging Accelerates Adoption
Suggested endorsements
Skill recommendations
Skill marketing
©2012 LinkedIn Cororation. All Rights Reserved.
Virality only
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Outline
Skill discovery
Skill tagging
Skill recommendations
Suggested endorsements
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Skill Discovery: Unsupervised Topics from Profiles
Extract
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Topic Clustering & Phrase Sense Disambiguation
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Deduplication Signals from Mechanical Turk
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Sample Task for Mechanical Turk Workers
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Skill Phrase Deduplication
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Outline
Skill discovery
Skill tagging
Skill recommendations
Suggested endorsements
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Lead designer and engineer for the implementation of a user-centric, fully-configurable UI for data aggregation and reporting.Developed over 20 SaaS custom applications using Python, Javascript and RoR.
Tagging Skill Phrases
Tagging: Extract potential skill phrases from text
Standardize unambiguous phrase variants
JavaScript RoR SaaS Python
ror
rubyonrails
ruby on rails development
ruby rails
ruby on rail
Ruby on Rails
Document (ex: Profile)
Tokenization
Skills Tagger
Phrases
(up to 6 words)
Skills Classifier
Skills
(unordered)
Skills
(ranked by relevance)
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Outline
Skill discovery
Skill tagging
Skill recommendations
Suggested endorsements
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Skill Inference
How suggested/inferred skills work:
– The skill likelihood is a conditional model
– Probabilities are combined using a Naïve Bayes Classifier
If you are an engineer at Apple, you probably know about iPhone Development.
Profile
Extract attributes
- Company ID
- Title ID
- Groups ID
- Industry ID
- …
Skills Classifier
Skills
(ranked by likelihood)
Feature
Vectors
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Skill Recommendations for Your LinkedIn Profile
49% Conversion
4% Conversion
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Outline
Skill discovery
Skill tagging
Skill recommendations
Suggested endorsements
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Social Tagging via Skill Endorsements
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Social Tagging Accelerates Adoption
Skill endorsements
Skill recommendations
Skill marketing
©2012 LinkedIn Cororation. All Rights Reserved.
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Data Amplifies Desire
1. Desire + Social Proof
2. Viral Loops + Network Effects
3. Data Catalyst + Recommendation Algorithms
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Over 58 Million Profiles are now Tagged with Skills
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All This Data Flows Back Into Our Lucene Index
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Helping us Connect Talent & Opportunity
TimeLocation
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©2012 LinkedIn Corporation. All Rights Reserved.
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Questions?
We’re hiring: data.linkedin.com
@peteskomoroch
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CONTACTPete Skomoroch@peteskomoroch
http://data.linkedin.com