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Large-scale Social Recommendation Systems: Challenges and Opportunity

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Keynote talk at 4th International Workshop on Social Recommender Systems (SRS 2013) …

Keynote talk at 4th International Workshop on Social Recommender Systems (SRS 2013)
In conjunction with 22nd International World Wide Web Conference (WWW 2013). More details: http://cslinux0.comp.hkbu.edu.hk/~fwang/srs2013/

Published in: Internet

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  • 1. Large-Scale Social Recommendation Systems: Challenges And Opportunities Mitul Tiwari! ! Search, Network, and Analytics (SNA)! LinkedIn
  • 2. Who Am I 2
  • 3. Outline • About LinkedIn! • Social Recommender Systems at LinkedIn! • Social Graph Analysis! • Virality in Social Recommender Systems! • Scaling Challenges 3
  • 4. Linkedin By The Numbers 4 225M members 2 new members/sec
  • 5. Broad Range Of Products 5
  • 6. Member Profile 6
  • 7. Contacts 7
  • 8. Talent Solutions 8
  • 9. Job Search 9
  • 10. Company Pages 10
  • 11. Outline • About LinkedIn! • Social Recommender Systems at LinkedIn! • Social Graph Analysis! • Virality in Social Recommender Systems! • Scaling Challenges 11
  • 12. Linkedin Homepage • Powered by recommendations 12
  • 13. Recommender Ecosystem 13 ! Similar  Profiles Connections News Skill  Endorsements
  • 14. Outline • Social Recommender Systems at LinkedIn! • LinkedIn Today: Recommend News! • Jobs Recommendation! • Related Searches Recommendation! • Social Graph Analysis! • Virality in Social Recommender Systems! • Scaling Challenges 14
  • 15. Linkedin Today: News Recommendation • Objective: serve valuable professional news, leading to higher engagement as measured by metrics such as CTR 15
  • 16. News Recommendation: Explore/Exploit 16 item j from a set of candidates User i with user features (e.g., industry, behavioral features, Demographic features,……) (i, j) : response yijvisits Algorithm selects (click or not) Which item should we select? ! The item with highest predicted CTR ! An item for which we need data to predict its CTR Exploit Explore Agarwal et. al 2012
  • 17. News Recommendations: Challenges • Drop in CTR wrt Time 17
  • 18. News Recommendation: Challenges • Same item shown to the same users: drop in CTR 18
  • 19. News Recommendations: Revised Algorithm • Explore/Exploit scheme! • Explore: choose an item at random with a small probability (e.g., 5%)! • Exploit: choose highest scoring CTR item (e.g., 95%)! • Temporal smoothing: more weight to recent data! • Impression discounting: discount items with repeat views! • Segmented model: segment users in CTR estimation! • Opportunity: Multi-arm bandit problem 19
  • 20. Outline 20 • Social Recommender Systems at LinkedIn! • LinkedIn Today: Recommend News! • Jobs Recommendations! • Related Searches Recommendation! • Social Graph Analysis! • Social Update Stream and Virality! • Scaling Challenges
  • 21. Jobs Recommendation • Goal: recommend dream jobs to job seekers! • Challenges! • Lag between view and application, offer, acceptance! • High level of expectations 21
  • 22. Jobs Recommendation 22 17 Corpus StatsJob User Base Filtered title geo company industry description functional area … Candidate General expertise specialties education headline geo experience Current Position title summary tenure length industry functional area … Similarity (candidate expertise, job description) 0.56 Similarity (candidate specialties, job description) 0.2 Transition probability (candidate industry, job industry) 0.43 Title Similarity 0.8 Similarity (headline, title) 0.7 . . . derived Matching Binary Exact matches: geo, industry, … Soft transition probabilities, similarity, … Text Transition probabilities Connectivity yrs of experience to reach title education needed for this title … Ensemble Scorings Bhasin et. al 2012
  • 23. Magic Is In Feature Engineering • Open to relocation?! • Region similarity based on profile or network! • Region transition probability! • Predict members’ propensity to migrate and potential regions 23
  • 24. What Should You Transition To And When 24 • Probability of holding a title wrt time: spikes 12 months aligned Wang et. al, WWW’13
  • 25. Job Seeking: Socially Contagious 25 [Zhang, 2012] • Prob. of quitting increases as the #of recently left connected colleague
  • 26. Outline 26 • Social Recommender Systems at LinkedIn! • LinkedIn Today: Recommend News! • Jobs Recommendation! • Related Searches Recommendation! • Social Graph Analysis! • Social Update Stream and Virality! • Scaling Challenges
  • 27. Related Searches Recommendation • Millions of Searches everyday! • Help users to explore and refine their queries 27 Reda et. al, CIKM’12
  • 28. Related Searches Recommendation 28
  • 29. Related Searches Recommendations • Signals! • Collaborative Filtering! • Query-Result Click graph! • Overlapping terms! • Length-bias! • Ensemble approach for unified recommendation! • Practical considerations 29
  • 30. Related Searches Recommendations • Signals! • Collaborative Filtering! • Query-Result Click graph! • Overlapping terms! • Length-bias! • Ensemble approach for unified recommendation! • Practical considerations! • Opportunity: Build Personalized Search Recommendation 30
  • 31. Outline • About LinkedIn! • Social Recommender Systems at LinkedIn! • Social Graph Analysis! • Virality in Social Recommender Systems! • Scaling Challenges 31
  • 32. Link Prediction Over Social Graph 32
  • 33. Inmaps: Connection Graph 33
  • 34. Connection Strength • Measure strength of each connection! • Applications! • Introductions! • Update stream relevance 34
  • 35. Query-Result Clicks Graph • Application: Related Searches correlated by result clicks for related searches recommendation Q1 Qn R1 Rm 35
  • 36. Skills Similarity Graph • Graph of all co-occurrences between LinkedIn Skills 36
  • 37. Skills 37
  • 38. Find Influencers In Venture Capital? 38
  • 39. Outline • About LinkedIn! • Social Recommender Systems at LinkedIn! • Social Graph Analysis! • Virality in Social Recommender Systems! • Scaling Challenges 39
  • 40. Suggested Skill Endorsement 40
  • 41. Skills Endorsements 41
  • 42. Viral Growth: 1B Skills Endorsements • One of the fastest growing product in LinkedIn’s history 42
  • 43. Skill Tagging • Tagging: extract potential skills from profile using skills taxonomy! ! ! • Standardize skill phrase variants Profile Tokenize SkillsTagger Phrases Skills 43
  • 44. Skill Recommendation • Predict a skill even if not present in the profile! • Based on likelihood of member having a skill! • Features: company, industry, skills, ... 44 Profile Tokenize SkillsTagger Phrases Skills Skills Classifier Profile features Recommended Skills
  • 45. Suggested Skill Endorsements • Binary Classification! • Features! • Company overlap, School overlap, Industrial and functional area similarity, Title similarity, Site interactions, Co- interactions, ... Candidate generation Classifier Features - Company - Title - Industry ... Suggested Endorsements 45
  • 46. Social Recommendation And Tagging SkillTagging Skill Recommendation Suggested Skill Endorsements 46
  • 47. Skills Important For Data Scientists? 47
  • 48. Outline • About LinkedIn! • Social Recommender Systems at LinkedIn! • Social Graph Analysis! • Virality in Social Recommender Systems! • Scaling Challenges 48
  • 49. Scaling Challenges: Related Searches Example • Kafka: publish-subscribe messaging system! • Hadoop: MapReduce data processing system ! • Azkaban: Hadoop workflow management tool! • Voldemort: Key-value store Metaphor Hadoop Search Backend Kafka Voldemort Related Searches Backend Front End HDFS 49
  • 50. Outline • About LinkedIn! • Social Recommender Systems at LinkedIn! • Social Graph Analysis! • Virality in Social Recommender Systems! • Scaling Challenges 50
  • 51. References 51
  • 52. Acknowledgement • Thanks to Data Team at LinkedIn: http://data.linkedin.com! • We are hiring!! • Contact: mtiwari[at]linkedin.com! • Follow: @mitultiwari on Twitter 52 You! Applied Reseacher/ Research Engineer
  • 53. Questions? 53