Keynote Peter Skomoroch - skills, reputation, and search
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This document discusses how LinkedIn uses skills data and endorsements to connect talent with opportunities. It describes how LinkedIn gathered over 1 billion endorsements in 5 months through viral growth loops and network effects. Skill discovery is done through unsupervised topic modeling of profile text. Skills are then recommended to users based on their profiles and a naïve Bayes classifier. Social tagging of skills is accelerated through skill endorsements and recommendations.
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Keynote Peter Skomoroch - skills, reputation, and search
- 1. Skills, Reputation, and Search Pete Skomoroch Principal Data Scientist, LinkedIn
- 2. Vision: Create Economic Opportunity for Every Professional 2 Location
- 3. LinkedIn: The Professional Profile of Record ©2012 LinkedIn Corporation. All Rights Reserved. 3 200+MMembers 200M Member Profiles
- 4. LinkedIn Search: Connecting Talent with Opportunity 4
- 5. Skills Correlated with the Job Title “Data Scientist” 5
- 6. Skills Related to “Big Data” 6
- 9. 9
- 14. Reputation: Building the Endorsement Graph 14
- 15. 15 Viral Growth: 1 Billion Endorsements in 5 Months
- 16. How Did We Gather this Data? 16 1. Desire + Social Proof 2. Viral Loops + Network Effects 3. Data Foundation + Recommendation Algorithms
- 17. 17 1) Desire & Social Proof
- 18. A endorses B B notified B “accepts” endorsement B endorses C B endorses D Endorsement recommendations Email NotificationNews Feed 2) Viral Loops & Network Effects
- 19. 3) Data Foundation: Skills & Suggested Skills 19
- 20. Data Foundation: LinkedIn Skills 20
- 21. Social Tagging Accelerates Adoption Suggested endorsements Skill recommendations Skill marketing ©2012 LinkedIn Cororation. All Rights Reserved. Virality only
- 22. Outline 22 Skill discovery Skill tagging Skill recommendations Suggested endorsements
- 23. Skill Discovery: Unsupervised Topics from Profiles 23 Extract
- 24. Topic Clustering & Phrase Sense Disambiguation 24
- 25. Deduplication Signals from Mechanical Turk 25
- 26. Sample Task for Mechanical Turk Workers 26
- 28. Outline 28 Skill discovery Skill tagging Skill recommendations Suggested 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 variants 29 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)
- 30. Outline 30 Skill discovery Skill tagging Skill recommendations Suggested endorsements
- 31. 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. 31 Profile Extract attributes - Company ID - Title ID - Groups ID - Industry ID - … Skills Classifier Skills (ranked by likelihood) Feature Vectors
- 37. Skill Recommendations for Your LinkedIn Profile 37 49% Conversion 4% Conversion
- 38. Outline 38 Skill discovery Skill tagging Skill recommendations Suggested endorsements
- 39. Social Tagging via Skill Endorsements 39
- 40. Social Tagging Accelerates Adoption Skill endorsements Skill recommendations Skill marketing ©2012 LinkedIn Cororation. All Rights Reserved.
- 41. Data Amplifies Desire 41 1. Desire + Social Proof 2. Viral Loops + Network Effects 3. Data Catalyst + Recommendation Algorithms
- 42. Over 58 Million Profiles are now Tagged with Skills 42
- 43. All This Data Flows Back Into Our Lucene Index 43
- 44. Helping us Connect Talent & Opportunity 44 Location
- 45. Questions? We’re hiring: data.linkedin.com @peteskomoroch ©2012 LinkedIn Corporation. All Rights Reserved. 45