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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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2 4 8

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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/