Socializing Search. Professionally. Sriram Sankar
Principal Staff Engineer Recruiting Solutions Daniel Tunkelang Head, Query Understanding

Whether you’ve tried to find
an Apache committer…

…or an Apache commander,
3

you’ve probably used LinkedIn Search.
4

Let’s talk about… • Infrastructure
• Quality 5

LinkedIn Search leverages the economic
graph. 6

Social means that relevance is
highly personalized. 7

Machine-learned ranking, socially.  Relevance
models incorporate user features: score = P (Document | Query, User)  Our model: tree with logistic regression leaves. X2=? b0 + b1 T(x1 )+...+ bn xn X10< 0.1234 ? a0 + a1 P(x1 )+...+ anQ(xn ) g 0 + g1 R(x1 )+...+ g nQ(xn ) 8

LinkedIn’s focus: entity-oriented search. Company
Name Search Employees Jobs 9

Query understanding can act as
a relevance filter. 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 10

Less is more. warren buffett
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Coming soon: entity-driven search assist.
link Jobs at LinkedIn People currently working at LinkedIn People who used to work at LinkedIn Search

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 13

1. 2. BLAH BLAH BLAH
BLAH BLAH Daniel Daniel BLAH BLAH LinkedIn BLAH BLAH BLAH BLAH Sriram BLAH LinkedIn BLAH BLAH BLAH BLAH BLAH BLAH BLAH Sriram LinkedIn 1 2 Inverted Index Forward Index 14

A standard scoring capability is
built in 15

 Extremely easy to build
a search engine  But difficult to get sophisticated 16

The LinkedIn Search Stack Request
Live Updates Updates Query Rewriter Index Retrieval Scorer Offline Data Building Data Sorter/Blender Response 17

Search Index Served by Lucene
 Inverted index  Forward index  Static rank based document ordering 18

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 19

Live Data Updates  Feed
based framework to support updates to offline data builds  Lucene enhanced with a partial index update capability 20

Query Rewriting (and Planning) 
Accepts raw query and user metadata  Produces Lucene retrieval query and metadata for scoring  May use data models built offline 21

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 22

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 23

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. 24

Sriram Sankar ssankar@linkedin.com https://linkedin.com/in/sriramxsankar Daniel
Tunkelang dtunkelang@linkedin.com https://linkedin.com/in/dtunkelang 25