find and be found: information retrieval at linkedin
DESCRIPTION
Find and Be Found: Information Retrieval at LinkedIn SIGIR 2013 Industry Track Presentation http://sigir2013.ie/industry_track.html LinkedIn has a unique data collection: the 200M+ members who use LinkedIn are also the most valuable entities in our corpus, which consists of people, companies, jobs, and a rich content ecosystem. Our members use LinkedIn to satisfy a diverse set of navigational and exploratory information needs, which we address by leveraging semi-structured and social content to understanding their query intent and deliver a personalized search experience. In this talk, we will discuss some of the unique challenges we face in building the LinkedIn search platform, the solutions we've developed so far, and the open problems we see ahead of us. Shakti Sinha heads LinkedIn's search relevance team, and has been making key contributions to LinkedIn's search products since 2010. He previously worked at Google as both a research intern and a software engineer. He has an MS in Computer Science from Stanford, as well as a BS degree from College of Engineering, Pune. Daniel Tunkelang leads LinkedIn's efforts around query understanding. Before that, he led LinkedIn's product data science team. He previously led a local search quality team at Google and was a founding employee of Endeca (acquired by Oracle in 2011). He has written a textbook on faceted search, and is a recognized advocate of human-computer interaction and information retrieval (HCIR). He has a PhD in Computer Science from CMU, as well as BS and MS degrees from MIT.TRANSCRIPT
Recruiting Solutions Recruiting Solutions Recruiting Solutions
formation Retrieval at LinkedIn Shakti Sinha Daniel Tunkelang Head, Search Relevance Head, Query Understanding
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Shakti Daniel
Find and be Found:
Why do 225M+ people use LinkedIn?
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Profile: the professional identity of record.
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Job recommendations.
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Publishing platform for professional content.
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Search helps members find and be found.
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Search for people,
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Search for people, jobs,
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Search for people, jobs, groups, and more.
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Every search is personalized.
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Let’s talk a bit about how it all works.
§ Query Understanding
§ Ranking More at http://data.linkedin.com/search.
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Query Understanding
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Daniel Tunkelang Head, Query Understanding
Pre-retrieval: segment and tag queries.
lucene software engineer
lucene “software engineer”
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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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.
Query expansion for exploratory queries.
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software patent lawyer
Query expansions derived from reformulations.
e.g., lawyer -> attorney
Understanding misspelled queries.
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daniel tankalong infomation retrieval
marisa meyer ingenero eletrico
jonathan podemsky desenista industrail
Did you mean daniel tunkelang?
Did you mean marissa mayer?
Did you mean johnathan podemsky?
Did you mean information retrieval?
Did you mean ingeniero electrico?
Did you mean desenhista industrial?
Spelling out the details.
entity data people, companies
successful queries tunkelang =>
reformulations marisa => marissa
n-grams dublin => du ub bl li in
metaphones mark/marc => MRK
word pairs johnathan podemsky
INDEX } { marisa meyer yoohoo
marissa
marisa
meyer
mayer
yahoo
yoohoo
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Ranking
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Shakti Sinha Head, Search Relevance
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.
Search log data has biases.
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§ Presentation bias – Results shown higher tend to get clicked more often. – Use FairPairs [Radlinski and Joachims, AAAI’06].
not flipped
flipped
flipped
Clicked!
✗
✔
✔
✗
✗
✗
training data
Search log data has biases.
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§ Sample bias – User clicks or skips only what is shown. – What about low scoring results from existing model? – Add low-scoring results as ‘easy negatives’ so model
learns bad results not presented to user.
…
label 0
label 0
label 0
label 0
…
page 1 page 2 page 3 page n
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How to train your model.
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 )
Take-Aways
§ LinkedIn’s search problem is unique because of deep role of personalization – users are integral part of the corpus.
§ Query understanding allows us to optimize for entity-oriented search against semi-structured content.
§ Ranking requires us to contextually apply global and personalized user, query, and document features.
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Thank you!
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225,
Want to learn more?
§ Check out http://data.linkedin.com/search.
§ Contact us:
– Shakti: [email protected] http://linkedin.com/in/sdsinha
– Daniel: [email protected] http://linkedin.com/in/dtunkelang
– Asif: [email protected] http://linkedin.com/in/asifmakhani
§ Did we mention that we’re hiring?
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