Search plays an important role in online social networks as it provides an essential mechanism for discovering members and content on the network. Related search recommendation is one of several mechanisms used for improving members’ search experience in finding relevant results to their queries. This paper describes the design, implementation, and deployment of Metaphor, the related search recommendation system on LinkedIn, a professional social networking site with over 175 million members worldwide. Metaphor builds on a number of signals and filters that capture several dimensions of relatedness across member search activity. The system, which has been in live operation for over a year, has gone through multiple iterations and evaluation cycles. This paper makes three contributions. First, we provide a discussion of a large-scale related search recommendation system. Second, we describe a mechanism for effectively combining several signals in building a unified dataset for related search recommendations. Third, we introduce a query length model for capturing bias in recommendation click behavior. We also discuss some of the practical concerns in deploying related search recommendations.

1. Metaphor: A System for Related
Searches Recommendations
Mitul Tiwari
Joint work with Azarias Reda, Yubin Park, Christian Posse, and
Sam Shah
LinkedIn

2. Who am I

3. Outline
• Related Searches at LinkedIn
• Metaphor: A System for Related Searches
Recommendations
• Design
• Implementation
• Evaluation

4. LinkedIn by the numbers
• 175M+ members
• 2+ new user registrations per second
• 4.2 Billion people searches in 2011
• 9.3 Billion page views in Q2 2012
• 100+ million monthly active users in Q2 2012

5. Related Searches at LinkedIn
• Millions of searches everyday
• Goal: Build related searches system at LinkedIn
• To help users to explore and refine their queries
Azarias Reda, Yubin Park, Mitul Tiwari, Christian Posse, and Sam Shah

6. Related Searches at LinkedIn

7. Metaphor: A Related Searches
System
• Design
• Implementation
• Evaluation

8. Metaphor: A Related Searches
System
• Design
• Implementation
• Evaluation

9. Design
• Signals
• Collaborative Filtering
• Query-Result Click graph
• Overlapping terms
• Length-bias
• Ensemble approach for unified recommendation
• Practical considerations

10. Design: Collaborative Filtering
• Searches correlated by time
• Searches done in the same session by the same user
• Collaborative filtering: implicit feedback
• TFIDF scoring to take care of popular queries (e.g. `Obama’)
Q1 Q2 Q3
Time

11. Design: Query-Result Clicks
• Searches correlated by result clicks
Q1
Qn
R1
Rm

12. Design: Overlapping Terms
• Searches with overlapping terms
• TFIDF scoring to give importance to terms
Software Developer
Software Engineer
Q1
Q2

13. Design: Length Bias
• Insight: clicks on suggestions one term longer

14. Design: Length Bias
• Insight: clicks on suggestions one term longer
• Corresponds to refining the initial query
• Statistical biasing model to score a longer query higher

15. Design: Ensemble Approach
• Need to generate unified recommendation dataset
• Analysis to figure out engagement of each signal
• Attempted ML approach
• Minimal overlap across different signals

16. Design: Ensemble Approach
• Step-wise unionization
• Importance based on individual signal performance
• First, collaborative filtering
• Second, queries correlated by query-result clicks
• Third, queries overlapping terms

17. Design: Practical Considerations
• System designed for public consumption
• Strong profanity filters
• Need to deal with misspellings
• Languages
• Remove spammy search queries

18. Metaphor: A Related Searches
System
• Design
• Implementation
• Evaluation

19. Implementation Challenge
• Scale
• 175M+ members
• Billions of searches
• Terabytes of data to process

20. Implementation
• Kafka: publish-subscribe messaging system
• Hadoop: MapReduce data processing system
• Azkaban: Hadoop workflow management tool
• Voldemort: Key-value store

21. Implementation: Workflow

22. Metaphor: A Related Searches
System
• Design
• Implementation
• Evaluation

23. Evaluation
• Performance of each signal and combination
• How does the system scale?

24. Evaluation Cont’d
• Offline evaluation
• Precision-Recall
• Online evaluation
• A/B testing to measure engagement
• Performance evaluation

25. Offline Evaluation
• Correct set: set of searches performed by a user in the
following K minutes, here K=10

26. Online Evaluation
• Used A/B testing
• Metrics
• Coverage: queries with recommendations
• Impressions: # of recommendations shown
• Clicks: Clicks on recommendations
• Click-through rate (CTR): Clicks per impression

27. Online Evaluation

28. Evaluation: System Runtime

29. Selected References
• R. Baeza-Yates. Graphs from search engine queries. LNCS, 4362:1–8, 2007.
• P. Boldi, F. Bonchi, C. Castillo, D. Donato, and S. Vigna. Query suggestions using query-
flow graphs. In Proceedings of the WSDM, 2009.
• M. A. Hasan, N. Parikh, B. Singh, and N. Sundaresan. Query suggestion for E-commerce
sites. In Proceedings of the WSDM, 2011.
• Q. Mei, D. Zhou, and K. Church. Query suggestion using hitting time. In Proceedings of
the CIKM, 2008.
• Z. Zhang and O. Nasraoui. Mining search engine query logs for query recommendation.
In Proceedings of the WWW, 2006.

30. Questions?