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

  • 1. Skills, Reputation, and SearchPete SkomorochPrincipal Data Scientist, LinkedIn
  • 2. Vision: Create Economic Opportunity for Every Professional2Location
  • 3. LinkedIn: The Professional Profile of Record©2012 LinkedIn Corporation. All Rights Reserved. 3200+MMembers 200M MemberProfiles
  • 4. LinkedIn Search: Connecting Talent with Opportunity4
  • 5. Skills Correlated with the Job Title “Data Scientist”5
  • 6. Skills Related to “Big Data”6
  • 7. Information Retrieval7
  • 8. Soul Retrieval8
  • 9. 9
  • 10. Lucene on LinkedIn10
  • 11. Lucene Endorsement Graph11
  • 12. Solr on LinkedIn12
  • 13. Solr Endorsement Graph13
  • 14. Reputation: Building the Endorsement Graph14
  • 15. 15Viral Growth: 1 Billion Endorsements in 5 Months
  • 16. How Did We Gather this Data?161.  Desire + Social Proof2.  Viral Loops + Network Effects3.  Data Foundation + Recommendation Algorithms
  • 17. 171) Desire & Social Proof
  • 18. AendorsesBBnotifiedB “accepts”endorsementBendorsesCBendorsesDEndorsementrecommendationsEmail NotificationNews Feed2) Viral Loops & Network Effects
  • 19. 3) Data Foundation: Skills & Suggested Skills19
  • 20. Data Foundation: LinkedIn Skills20
  • 21. Social Tagging Accelerates AdoptionSuggestedendorsementsSkill recommendationsSkill marketing©2012 LinkedIn Cororation. All Rights Reserved.Virality only
  • 22. Outline22Skill discoverySkill taggingSkill recommendationsSuggested endorsements
  • 23. Skill Discovery: Unsupervised Topics from Profiles23Extract
  • 24. Topic Clustering & Phrase Sense Disambiguation24
  • 25. Deduplication Signals from Mechanical Turk25
  • 26. Sample Task for Mechanical Turk Workers26
  • 27. Skill Phrase Deduplication27
  • 28. Outline28Skill discoverySkill taggingSkill recommendationsSuggested endorsements
  • 29. 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)
  • 30. Outline30Skill discoverySkill taggingSkill recommendationsSuggested endorsements
  • 31. 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
  • 32. Skill Recommendations for Your LinkedIn Profile3749% Conversion4% Conversion
  • 33. Outline38Skill discoverySkill taggingSkill recommendationsSuggested endorsements
  • 34. Social Tagging via Skill Endorsements39
  • 35. Social Tagging Accelerates AdoptionSkill endorsementsSkill recommendationsSkill marketing©2012 LinkedIn Cororation. All Rights Reserved.
  • 36. Data Amplifies Desire411.  Desire + Social Proof2.  Viral Loops + Network Effects3.  Data Catalyst + Recommendation Algorithms
  • 37. Over 58 Million Profiles are now Tagged with Skills42
  • 38. All This Data Flows Back Into Our Lucene Index43
  • 39. Helping us Connect Talent & Opportunity44Location
  • 40. Questions?We’re hiring: data.linkedin.com@peteskomoroch©2012 LinkedIn Corporation. All Rights Reserved. 45
  • 41. CONTACTPete Skomoroch@peteskomorochhttp://data.linkedin.com