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.
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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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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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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