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Social Search in a Professional Context
Daniel Tunkelang LinkedIn, Head of Query Understanding
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Daniel
Workshop on Data-driven User Behavioral Modeling and Mining from Social Media
LinkedIn connects talent to opportunity.
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Search enables the participants in the economic graph to find and be found.
Overview
Why do people search in a professional context?
How do we help people search in
a professional context?
Next play?
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Scenario 1: Pleased to meet you!
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People search isn’t the same as web search.
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LinkedIn works hard to make it effortless.
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Even harder to reduce user effort to a few chars.
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Searchers use what they know to find people.
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Not all navigational queries are name searches.
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Scenario 2: Looking for new opportunities.
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Lots of jobs in DC for data scientists.
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My connections can help me get a $100k+ job.
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Apply, contact the recruiter, or seek a referral.
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Scenario 3: I know what I want when I see it.
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Another year, another CIKM industry event.
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We’ll need student volunteers, too.
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And some sponsors!
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LinkedIn’s focus: entity-oriented search.
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Company
Employees
Jobs
Name Search
Query tagging: key to query understanding.
§ Using human judgments to evaluate tag precision. – Extremely accurate (> 99%) for identifying person names. – Harder to distinguish company vs. title vs. skill (e.g., oracle dba).
§ Comparing CTR for tag matches vs. non-matches. – Difference can be large enough to suggest filtering vs. ranking:
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Query Tagging: An Example
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Detecting navigational vs. exploratory queries.
Pre-retrieval § Sequence of query tags.
Post-retrieval § Distribution of scores / features.
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Click behavior § Title searches >50x more
likely to get 2+ clicks than name searches.
Navigation vs. Exploration: Behavior Patterns
§ Exploratory searches leads to ~5x more clicks per search than navigational searches.
§ Clicks on 2nd-degree connection more than 2x as likely to lead to invitation from exploratory vs. navigational search.
§ For navigational queries, 1st degree > 2nd degree > …
§ For exploratory queries, 2nd and 3rd degree > 1st degree.
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Query expansion for exploratory queries.
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software patent lawyer
Query expansions derived from reformulations.
e.g., lawyer -> attorney
LinkedIn search is personalized.
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kevin scott
But global factors matter.
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Relevant results can be in or out of network.
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§ Searcher’s network matters for relevance. – Within network results have higher CTR.
§ But the network is not enough. – About two thirds of search clicks come from out of
network results.
Personalized machine-learned ranking.
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§ Data point is a triple (searcher, query, document). – Searcher features are important!
§ Labels: Is this document relevant to the query and the user? – Depends on the user’s network, location, etc. – Too much to ask random person to judge.
§ Training data has to be collected from search logs.
How to train your model.
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§ Train simple models to resemble complex ones. – Build Additive Groves model [Sorokina et al, ECML ’07],
which is good at detecting interactions. § Build tree with logistic regression leaves.
§ By restricting tree to user and query features, only regression model evaluated for each document.
β0 +β1T (x1)+...+βn xn
α0 +α1P(x1)+...+αnQ(xn )
X2=?
X10< 0.1234 ?
γ0 +γ1R(x1)+...+γnQ(xn )
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Make search truly entity-centric.
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results
results
Use the search box to surface task intent.
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I am…
looking for a job… at LinkedIn in Fiji trying to hire…
software engineers web developers
interested in learning about… Hadoop NoSQL
It takes two to connect talent to opportunity.
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LinkedIn: connecting talent to opportunity.
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Search: enabling the participants in the economic graph to find and be found.
Thank you!
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238,
Want to learn more?
§ Check out http://data.linkedin.com/search.
§ Contact me: [email protected]
http://linkedin.com/in/dtunkelang
§ Did I mention that we’re hiring? J
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