Socializing Search. Professionally.

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Sriram SankarDaniel TunkelangPrincipal Staff Engineer Head, Query Understanding 1

Sriram Daniel

Socializing Search. Professionally.

Whether you’ve tried to find an Apache committer…

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…or an Apache commander,

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you’ve probably used LinkedIn Search.

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Let’s talk about…

• Infrastructure • Quality

Sriram Daniel

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LinkedIn Search leverages the economic graph.

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Social means that relevance is highly personalized.

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Machine-learned ranking, socially.

Relevance models incorporate user features:

score = P (Document | Query, User)

Our model: tree with logistic regression leaves.

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X 2=0

X2=?

X2=1

X10< 0.1234 ?

Yes

No

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LinkedIn’s focus: entity-oriented search.

Company

Employees

Jobs

Name

Search

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Query understanding can act as a relevance filter.

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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

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Less is more.

warren buffett

Jobs at LinkedIn

Searchlink

People currently working at LinkedIn

People who used to work at LinkedIn

Coming soon: entity-driven search assist.

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Infrastructure

Lucene Map of terms to documents – the index Provides an API to add and remove documents to the

index Provides an API to query the index

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BLAH BLAH BLAH Daniel BLAH BLAH LinkedIn BLAH BLAH BLAH BLAH

BLAH BLAH Sriram BLAH LinkedIn BLAH BLAH BLAH BLAH BLAH BLAH BLAH2.

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Daniel Sriram LinkedIn

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Inverted Index Forward Index

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A standard scoring capability is built in

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Extremely easy to build a search engine

But difficult to get sophisticated

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The LinkedIn Search Stack

Query Rewriter

Index Retrieval

Scorer

Sorter/Blender

Request

Response

OfflineData

Building

Updates

LiveUpdates

Data

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Search Index Served by Lucene

Inverted index Forward index Static rank based document ordering

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Offline Data Builds on Hadoop

Multi-stage map-reduce pipeline allows complex data processing

Produces sharded single segment Lucene index with documents sorted by static rank

Produces data models for use in query rewriting

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Live Data Updates

Feed based framework to support updates to offline data builds

Lucene enhanced with a partial index update capability

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Query Rewriting (and Planning)

Accepts raw query and user metadata Produces Lucene retrieval query and metadata for

scoring May use data models built offline

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Index Retrieval

Lucene query built by query rewriter is used to retrieve documents from the Lucene index

Documents are retrieved in static rank order (best document first)

Retrieval may be early-terminated – given that retrieval is in static rank order

No scoring is performed during retrieval

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Scoring

Scoring is performed after retrieval Its input is the retrieved document (i.e., includes the

forward index), a description of how the retrieval query matched the document, and the scoring metadata produced by the rewriter

Costly features can be computed offline during the index building process in Hadoop – e.g., tf/idf calculations

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Summary

Quality LinkedIn Search leverages the economic graph. Social means that relevance is highly personalized. Less is more: query understanding is a relevance filter. Moving in the direction of suggesting structured queries.

System Powered by Lucene, but with additional components. Offline data builds on Hadoop, partial index updates. Index uses static ranking and early termination. Scoring performed outside of Lucene.

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Sriram SankarDaniel Tunkelangssankar@linkedin.com dtunkelang@linkedin.comhttps://linkedin.com/in/sriramxsankar https://linkedin.com/in/dtunkelang