Socializing Search. Professionally. Sriram Sankar and Daniel Tunkelang Presented at the O'Reilly Strata 2014 Conference LinkedIn has a unique data collection: the 277M+ 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. As a result, we’ve built a system quite different from those used for web or enterprise search. In this talk, we will discuss how we have addressed the unique scalability, performance, and search quality challenges in order to deliver billions of deeply personalized searches to our members. Although many of the challenges we face are unique to LinkedIn, we hope that the ideas we share will prove useful to other folks thinking about entity-oriented search or working with large-scale social network data.

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

2. Whether you’ve tried to find an Apache committer…

3. …or an Apache commander,
3

4. you’ve probably used LinkedIn Search.
4

5. Let’s talk about…
• Infrastructure
• Quality
5

6. LinkedIn Search leverages the economic graph.
6

7. Social means that relevance is highly personalized.
7

8. 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

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

10. 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

11. Less is more.
warren buffett
11

12. Coming soon: entity-driven search assist.
link
Jobs at LinkedIn
People currently working at LinkedIn
People who used to work at LinkedIn
Search

13. 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

14. 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

15. A standard scoring capability is built in
15

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

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

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

19. 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

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

21. 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

22. 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

23. 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

24. 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

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