Content, Connections, and Context Daniel Tunkelang, LinkedIn Keynote at Workshop on Recommender Systems and the Social Web At 6th ACM International Conference on Recommender Systems (RecSys 2012) Recommender systems for the social web combine three kinds of signals to relate the subject and object of recommendations: content, connections, and context. Content comes first - we need to understand what we are recommending and to whom we are recommending it in order to decide whether the recommendation is relevant. Connections supply a social dimension, both as inputs to improve relevance and as social proof to explain the recommendations. Finally, context determines where and when a recommendation is appropriate. I'll talk about how we use these three kinds of signals in LinkedIn's recommender systems, as well as the challenges we see in delivering social recommendations and measuring their relevance.

1. Daniel
Content, Connections, and Context
Daniel Tunkelang
Principal Data Scientist at LinkedIn
Recruiting Solutions 1

2. Recommendation Products at LinkedIn
Similar Profiles
Connections
Network updates
Events You May
Be Interested In
News
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3. More than 50%
Recommendations drive:
> 50% of connections
> 50% of job applications
> 50% of group joins
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4. Inputs for Recommender Systems
Social Graph
Content
Behavior
Queries
Page Views
Actions
…
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5. Take-Aways
Content is king.
Connections provide social dimension.
Context determines where and when
a recommendation is appropriate.
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6. What is the goal of recommendations?
O Growth
O Engagement
O Revenue
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7. What is the goal of recommendations?
O Growth
O Engagement
O Revenue
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8. Users First!
Provide relevant content
and establish social connections
in appropriate context.
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9. Content is King
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10. Goal-Seeking is about Content
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11. Content (Ir)relevance
§ No right answer, but many wrong answers.
%no WTF!
[Voorhees, 2004] @k
http://bit.ly/percentno http://bit.ly/wtfatk
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12. Example: Related Searches
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13. Collaborative Filtering as Content Signal
§ Use temporal locality within sessions.
§ Find queries with clicks on similar results.
§ Look for query overlap.
§ Learn more at CIKM! [Reda et al, 2012]
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14. Example: Jobs You Might Be Interested In
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15. Content Signals Dominate Social Signals
Job Corpus Stats
Matching Transition probabilities
Connectivity
Binary yrs of experience to reach title
title industry …
Exact matches: education needed for this title
geo description …
company functional area geo, industry,
…
User Base Soft Similarity
(candidate expertise, job description)
transition
Filtered 0.56
probabilities,
Similarity
Candidate similarity, (candidate specialties, job description)
… 0.2
Transition probability
Text (candidate industry, job industry)
General Current Position 0.43
expertise title
specialties summary Title Similarity
education tenure length 0.8
headline industry
Similarity (headline, title)
geo functional area
experience … 0.7
.
derive
d
.
.
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16. Summary
Provide relevant content
and establish social connections
in appropriate context.
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17. Connections
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18. Connections are Social Dimensions
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19. Example: People You May Know
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20. Relationships are Social
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21. Shared Connections as a Signal
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22. The Power of Social Proof
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23. Beyond Triadic Closure
§ Triads suggest and affect relationships.
[Simmel, 1908], [Granovetter, 1973]
§ Triangle closing is a Big Data problem.
[Shah, 2011]
§ Use machine learning to rank candidates.
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24. Summary
Provide relevant content
and establish social connections
in appropriate context.
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25. Context
"to every thing there is a season”
[Ecclesiastes 3:1]
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26. One Platform, Many Users, Many Needs
http://blog.lab42.com/the-linkedin-profile
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27. No Weekend Meetings!
Weekdays
navigational queries
exploratory queries
Weekends
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28. Different Devices for Different Needs
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29. Time and Place for a Career Change
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30. Active vs. Passive Job Seekers
>10x more likely
to reply if active
[Rodriguez et al, 2012]
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31. Summary
Provide relevant content
and establish social connections
in appropriate context.
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32. Recap
Content is king.
Connections provide social dimension.
Context determines where and when
a recommendation is appropriate.
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33. Thank You!
175M+ 2/sec
62% non U.S.
25th
90 We’re Most visit website worldwide
(Comscore 6-12)
55
Hiring! >2M
Company pages
85%
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17
8
2 4 Fortune 500 Companies use
LinkedIn to hire
2004 2005 2006 2007 2008 2009 2010 2011
LinkedIn Members (Millions)
Learn more at http://data.linkedin.com/
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