View
41.565
Download
10
Category
Preview:
DESCRIPTION
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.
Citation preview
Recruiting Solutions Recruiting Solutions Recruiting Solutions
Content, Connections, and Context Daniel Tunkelang Principal Data Scientist at LinkedIn
Daniel
1
Recommendation Products at LinkedIn
2
Similar Profiles
Events You May Be Interested In
News
Network updates
Connections
More than 50%
3
Recommendations drive:
> 50% of connections > 50% of job applications > 50% of group joins
Take-Aways
5
Content is king.
Connections provide social dimension.
Context determines where and when a recommendation is appropriate.
Content (Ir)relevance
§ No right answer, but many wrong answers.
11
%no [Voorhees, 2004]
WTF! @k
http://bit.ly/wtfatk http://bit.ly/percentno
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]
13
Content Signals Dominate Social Signals
15
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 …
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.
23
Recap
32
Content is king.
Connections provide social dimension.
Context determines where and when a recommendation is appropriate.
Recommended