Post on 09-May-2015
description
Recruiting Solutions Recruiting Solutions Recruiting Solutions
Data By The People, For The People Daniel Tunkelang Director, Data Science LinkedIn
Daniel
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Why do 175M+ people use LinkedIn?
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Identity: find and be found
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Insights: discover and share knowledge
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People use LinkedIn because of other people.
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People as Users + People as Data
Unique opportunities and challenges! § Search § Recommendations § Networking
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Search
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People search is personal!
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But not all relevance factors are personal.
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Good Bad
People are semi-structured objects.
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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!
LinkedIn uses scale to derive structure.
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Software Developer
Social network is more than a ranking signal.
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People are a gateway to other entities.
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Search: Summary
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People finding people.
People being found.
People finding content.
Through other people.
Recommendations
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Recommendation products at LinkedIn
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Similar Profiles
Events You May Be Interested In
News
Network updates
Connections
LinkedIn’s recommender ecosystem
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Recommendations drive:
> 50% of connections > 50% of job applications > 50% of group joins
Inputs for recommender systems
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Content Social Graph
…
Behavior
Page Views Actions
Queries
Jobs You Might Be Interested In
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How LinkedIn matches people to jobs
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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 …
Is job-hunting socially contagious?
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[Posse, 2012]
Social referral
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Suggest based on connection strength and relevance to target user.
2x conversion!
[Amin et al, 2012]
Suggested skill endorsements
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Recommendations: Summary
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Content is king.
Connections provide social dimension.
Context determines where and when a recommendation is appropriate.
Networking
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People You May Know
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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
?
Shared connections as a signal
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Power of social proof
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More power of social proof
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…
Networking: Summary
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Close triangles to suggest connections.
Connections as social proof.
Unleash the power of weak ties.
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
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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!
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We’re
Hiring!
Learn more at http://data.linkedin.com/