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Data By The People, For The People

Data By The People, For The People

This CIKM 2012 Industry Event presentation discusses how LinkedIn utilizes data to enhance user experiences through search, recommendations, and networking. It emphasizes the importance of social connections in driving user engagement and job applications, highlighting that users benefit from being able to find and be found through others. Key takeaways include the role of social proof and machine learning in enhancing connection suggestions and user interactions on the platform.

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Daniel Tunkelang

May 21, 2026

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  1. Recruiting Solutions Recruiting Solutions Recruiting Solutions Data By The People,

    For The People Daniel Tunkelang Director, Data Science LinkedIn Daniel 1
  2. People as Users + People as Data Unique opportunities and

    challenges! §  Search §  Recommendations §  Networking 6
  3. People are semi-structured objects. 10 10 for i in [1..n]!

    s ← w1 w2 … wi ! if Pc (s) > 0! a ← new Segment()! a.segs ← {s}! a.prob ← Pc (s)! B[i] ← {a}! for j in [1..i-1]! for b in B[j]! s ← wj wj+1 … wi ! if Pc (s) > 0! a ← new Segment()! a.segs ← b.segs U {s}! a.prob ← b.prob * Pc (s)! B[i] ← B[i] U {a}! sort B[i] by prob! truncate B[i] to size k!
  4. Recommendation products at LinkedIn 16 16 Similar Profiles Events You

    May Be Interested In News Network updates Connections
  5. How LinkedIn matches people to jobs 20 Corpus Stats Job

    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 …
  6. Social referral 22 Suggest based on connection strength and relevance

    to target user. 2x conversion! [Amin et al, 2012]
  7. Recommendations: Summary 24 24 Content is king. Connections provide social

    dimension. Context determines where and when a recommendation is appropriate.
  8. Closing the triangles §  Triads suggest and affect relationships. [Simmel,

    1908], [Granovetter, 1973] §  Triangle closing is a Big Data problem. [Shah, 2011] §  Use machine learning to rank candidates. 27 Alice Bob Carol ?
  9. Conclusion §  People use LinkedIn because of other people. § 

    Primary use cases: – Find and be found. – Discover and share knowledge. §  People are at the heart of LinkedIn’s products: – Search – Recommendations – Networking 32
  10. 2 4 8 17 32 55 90 2004 2005 2006

    2007 2008 2009 2010 2011 LinkedIn Members (Millions) 175M+ 25th Most visit website worldwide (Comscore 6-12) Company pages >2M 62% non U.S. 2/sec 85% Fortune 500 Companies use LinkedIn to hire Thank You! 33 We’re Hiring! Learn more at http://data.linkedin.com/