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Keynote   Peter Skomoroch - skills, reputation, and search
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Keynote Peter Skomoroch - skills, reputation, and search

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    Keynote   Peter Skomoroch - skills, reputation, and search Keynote Peter Skomoroch - skills, reputation, and search Presentation Transcript

    • Skills, Reputation, and SearchPete SkomorochPrincipal Data Scientist, LinkedIn
    • Vision: Create Economic Opportunity for Every Professional2Location
    • LinkedIn: The Professional Profile of Record©2012 LinkedIn Corporation. All Rights Reserved. 3200+MMembers 200M MemberProfiles
    • LinkedIn Search: Connecting Talent with Opportunity4
    • Skills Correlated with the Job Title “Data Scientist”5
    • Skills Related to “Big Data”6
    • Information Retrieval7
    • Soul Retrieval8
    • 9
    • Lucene on LinkedIn10
    • Lucene Endorsement Graph11
    • Solr on LinkedIn12
    • Solr Endorsement Graph13
    • Reputation: Building the Endorsement Graph14
    • 15Viral Growth: 1 Billion Endorsements in 5 Months
    • How Did We Gather this Data?161.  Desire + Social Proof2.  Viral Loops + Network Effects3.  Data Foundation + Recommendation Algorithms
    • 171) Desire & Social Proof
    • AendorsesBBnotifiedB “accepts”endorsementBendorsesCBendorsesDEndorsementrecommendationsEmail NotificationNews Feed2) Viral Loops & Network Effects
    • 3) Data Foundation: Skills & Suggested Skills19
    • Data Foundation: LinkedIn Skills20
    • Social Tagging Accelerates AdoptionSuggestedendorsementsSkill recommendationsSkill marketing©2012 LinkedIn Cororation. All Rights Reserved.Virality only
    • Outline22Skill discoverySkill taggingSkill recommendationsSuggested endorsements
    • Skill Discovery: Unsupervised Topics from Profiles23Extract
    • Topic Clustering & Phrase Sense Disambiguation24
    • Deduplication Signals from Mechanical Turk25
    • Sample Task for Mechanical Turk Workers26
    • Skill Phrase Deduplication27
    • Outline28Skill discoverySkill taggingSkill recommendationsSuggested endorsements
    • 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 variants29JavaScript RoR SaaS Pythonrorrubyonrailsruby on rails developmentruby railsruby on railRuby on RailsDocument(ex: Profile)TokenizationSkills TaggerPhrases(up to 6 words)Skills ClassifierSkills(unordered)Skills(ranked by relevance)
    • Outline30Skill discoverySkill taggingSkill recommendationsSuggested endorsements
    • Skill Inference§  How suggested/inferred skills work:–  The skill likelihood is a conditional model–  Probabilities are combined using a Naïve BayesClassifier§  If you are an engineer at Apple, you probably knowabout iPhone Development.31ProfileExtractattributes- Company ID- Title ID- Groups ID- Industry ID- …Skills ClassifierSkills(ranked by likelihood)FeatureVectors
    • Skill Recommendations for Your LinkedIn Profile3749% Conversion4% Conversion
    • Outline38Skill discoverySkill taggingSkill recommendationsSuggested endorsements
    • Social Tagging via Skill Endorsements39
    • Social Tagging Accelerates AdoptionSkill endorsementsSkill recommendationsSkill marketing©2012 LinkedIn Cororation. All Rights Reserved.
    • Data Amplifies Desire411.  Desire + Social Proof2.  Viral Loops + Network Effects3.  Data Catalyst + Recommendation Algorithms
    • Over 58 Million Profiles are now Tagged with Skills42
    • All This Data Flows Back Into Our Lucene Index43
    • Helping us Connect Talent & Opportunity44Location
    • Questions?We’re hiring: data.linkedin.com@peteskomoroch©2012 LinkedIn Corporation. All Rights Reserved. 45
    • CONTACTPete Skomoroch@peteskomorochhttp://data.linkedin.com