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[In]formation Retrieval: Search at LinkedIn By Shakti Sinha & Daniel Tunkelang Bay Area Search Meetup Presentation March 27, 2013 http://www.meetup.com/Bay-Area-Search/events/63736862/ LinkedIn has a unique data collection: the 200M+ members who use LinkedIn are also part of the content those same members access using our information retrieval products. In this talk, the speakers will discuss some of the unique challenges we face in building the LinkedIn search platform, particularly around leveraging semi-structured and social content, understanding query intent, and personalizing relevance. 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 a 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.
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Recruiting Solutions Recruiting Solutions Recruiting Solutions
formation Retrieval: Search at LinkedIn Shakti Sinha Daniel Tunkelang Head, Search Relevance Head, Query Understanding
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Shakti Daniel
Why do 200M+ people use LinkedIn?
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People use LinkedIn because of other people.
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Search helps members find and be found.
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Rich collection of professional content.
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Every search is personalized.
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Let’s talk a bit about how it all works.
§ Query Understanding
§ Search Spam
§ Unified Search More at http://data.linkedin.com/search.
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Query Understanding
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People are semi-structured objects.
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for i in [1..n]! s ← w1 w2 … wi! if Pc(s) > 0! a ← new Segment()! a.segs ← {s}! a.prob ← Pc(s)! B[i] ← {a}! for j in [1..i-1]! for b in B[j]! s ← wj wj+1 … wi! if Pc(s) > 0! a ← new Segment()! a.segs ← b.segs U {s}! a.prob ← b.prob * Pc(s)! B[i] ← B[i] U {a}! sort B[i] by prob! truncate B[i] to size k!
Word sense is contextual.
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Understand queries as early as possible.
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Query structure has many applications.
§ Boost results that match query interpretation. § Bucket search log analysis by query classes. § Query rewriting specific to query classes. § …
Query understanding focuses on set-level metrics.
Not just about best answer, but getting to best question.
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Search Spam
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Let’s look at a search spammer.
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Summary is verbose but legitimate.
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But then comes the keyword stuffing.
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How we train our search spam classifier.
§ Find the queries targeted by spammers. – 10,000 most common non-name queries.
§ Look at top results for a generic user. – i.e., show unpersonalized search results.
§ Remove private profiles. – Members first! Can’t sacrifice privacy to fight spammers.
§ Label data by crowdsourcing. – Relevance is subjective, but spam is relatively objective.
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ROC curve for spam thresholding.
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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
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Spam score threshold
0 < a < b < 1
Integrate spamminess into relevance score.
§ Spam model yields a probability between 0 and 1.
§ Use spam score as piecewise linear factor: if score < spammin: # not a spammer relevance *= 1.0 elif score > spammax: # spammer relevance *= 0.0 else: # linear function of spamminess relevance *= (spammax - score) / (spammax - spammin)
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Spam is an arms race.
§ We can’t reveal precisely which features we use for spam detection, or spammers will work around them.
§ Spammers will try to reverse-engineer us anyway.
§ Personalization benefits us and our legitimate users – it’s hard to spam your way to high personalized ranking.
§ Fighting spam is all about making the investment less profitable for the spammer.
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Unified Search
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Un-Unified Search
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Introducing LinkedIn Unified Search!
Goal: make all of our content more discoverable.
Three new features: § Query Auto-Complete § Content Type Suggestions § Unified Search Result Page
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Query Auto-Complete
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Best completion not always the most popular.
§ In a heavy-tailed distribution, even the most popular queries account for a small fraction of distribution.
§ We don’t want to suggest generic queries that would produce useless results. – e.g., c -> company, j -> jobs
§ Goal is to not only to infer user’s intent but also suggest a search that yields relevant results across content types.
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Content Type Suggestions
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How we compute content type suggestions.
§ Rank content types by likelihood of a successful search. – Consider click-through behavior as well as downstream actions.
§ Bootstrap using what we know from pre-unified search behavior. – Tricky part is compensating for findability bias.
§ Continuously evaluate and collect feedback through user behavior. – E.g., members using the left rail to select a particular vertical.
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Unified Search Result Page
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Intent Detection and Page Construction
§ Relevance is now a two-part computation:
P(Content Type | User, Query) x
P(Document | User, Query, Content Type) § Intent detection comes first: inefficient to send all queries
to all verticals.
§ Secondary components introduce diversity.
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Summary
§ Personalize every search and leverage structure. § Understand queries as early as possible. § Fight the spammers that be. § Unify and simplify the search experience.
Goal: help LinkedIn’s 200M+ members find and be found.
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Thank you!
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Want to learn more?
§ Check out http://data.linkedin.com/search.
§ Contact us: – Shakti: ssinha@linkedin.com
http://linkedin.com/in/sdsinha
– Daniel: dtunkelang@linkedin.com http://linkedin.com/in/dtunkelang
§ Did we mention that we’re hiring?
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