Big data innovation_summit_2014
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This document discusses LinkedIn's job recommendation system. It begins with an overview of LinkedIn's size and role as a professional network. It then explains that job recommendations are made implicitly based on a member's profile rather than explicit searches. The recommendation algorithm analyzes a variety of member profile features to find similar jobs. Challenges include accurately resolving entities like companies and job titles that may be referred to differently, determining member location stickiness, and leveraging the professional network. The goal is to provide relevant job recommendations to members.
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Big data innovation_summit_2014
- 1. Recruiting SolutionsRecruiting SolutionsRecruiting Solutions Recommending Jobs You May Be Interested In Anuj Goyal Recommendations at LinkedIn Anuj
- 2. § About LinkedIn § Search vs. Recommendations § Recommendation Opportunities § Evaluating Job Recommendations § Job Recommendation Algorithm § Challenges § Summary 2 Overview
- 3. 270+ M Company Pages >3M * Professional searches in 2012 ~5.7B 90%Fortune 100 Companies use LinkedIn to hire * *as of March 31, 2014 New Members joining ~2/sec 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 3 World’s largest professional network Over 65% of members are now international
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- 9. 9 Jobs You May be Interested In
- 10. Evaluation § Upside metrics – Are users getting relevant jobs? § Downside metrics – Are users getting offending jobs? 10
- 11. Evaluation - Upside § Total Job Views (Clicks) § Total Applications § Total Viewers § Total Applicants 11
- 12. Evaluation - Downside § Applications per Click § Clicks per Impression § Applications per Impression § Expert Judgments 12
- 13. User Features & Recommendation Algorithm
- 15. Corpus StatsCandidate Jobs User Base 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 Recommendation Algorithm Transition probabilities Connectivity yrs of experience to reach title education needed for this title … 15 Job Collection
- 16. Challenges & Feature Engineering
- 18. ‘IBM’ has 13000+ variations - ibm – ireland - ibm research - T J Watson Labs - International Bus. Machines Are All Companies The Same? 18
- 19. - Software Engineer - Technical Yahoo - Member Technical Staff - Software Development Engineer - SDE Are All Titles The Same? 19
- 20. Same company with different name Same name but different companies Name Variations for IBM? “Orion” refers to 20 diff. companies large scale: 100M+ members, 2M+ company entities IBM: Intl Brotherhood of Magicians ~ 13000 Challenges – Entity Resolution 20
- 21. § Binary classifier (LR), not ranker § P({position, company entity} is a match) § Features § Content § Social § Behavior § Company candidate set leveraged from Social graph and cosine similarity 97% Precision at 50% Coverage Asonam’11, KDD’11 Challenges – Entity Resolution 21 Precision Coverage
- 22. Challenges – Geo Location 22
- 23. § Zip code mapped to Regions § How sticky are those locations? Feature Engineering – Sticky locations 23
- 24. § Open to relocation ? § Region similarity based on profiles or network § Region transition probability § Predict individuals propensity to migrate and most likely migration target Feature Engineering – Sticky locations 24
- 25. Feature Engineering – The Network effect 25
- 26. Hybrid Recommendation Title : Research Engineer Company : Yahoo! Location : CA,USA Skills : Stats, ML, Java Title : Data Scientist Company : Samsung Location : PA,USA Skills : Stats, R Title : Analyst Company : Microsoft Location : CA, USA Skills : R, ML Title : Research Engineer <1>, Data Scientist <1>, Analyst <1> Company : Yahoo<1>, Samsung<1>, Microsoft<1> Location : CA,USA <2>, PA,USA<1> Skills : Stats<2>, ML<2>, R<2>, Java<1> Applicant Features Distribution Data Scientist / Senior Data Scientist San Jose 26
- 27. Information Gain Pick Top K overrepresented features from the applicants distribution A representative projection of the job in the member feature space 27 Hybrid Recommendation
- 28. § Why Jobs Recommendations are Different § Recommendation Algorithm § Challenges – Entity Resolution – Location Resolution 28 Summary