2013 AUnifiedSearchFederationSystemb

(Jie et al., 2013) ⇒ Luo Jie, Sudarshan Lamkhede, Rochit Sapra, Evans Hsu, Helen Song, and Yi Chang. (2013). “A Unified Search Federation System based on Online User Feedback.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2488198

Subject Headings:

Notes

Cited By

Quotes

Author Keywords

Information retrieval; machine learning; search federation; user/machine systems

Abstract

Today's popular web search engines expand the search process beyond crawled web pages to specialized corpora ("verticals") like images, videos, news, local, sports, finance, shopping etc., each with its own specialized search engine. Search federation deals with problems of the selection of search engines to query and merging of their results into a single result set. Despite a few recent advances, the problem is still very challenging. First, due to the heterogeneous nature of different verticals, how the system merges the vertical results with the web documents to serve the user's information need is still an open problem. Moreover, the scale of the search engine and the increasing number of vertical properties requires a solution which is efficient and scaleable. In this paper, we propose a unified framework for the search federation problem. We model the search federation as a contextual bandit problem. The system uses reward as a proxy for user satisfaction. Given a query, our system predicts the expected reward for each vertical, then organizes the search result page (SERP) in a way which maximizes the total reward. Instead of relying on human judges, our system leverages implicit user feedback to learn the model. The method is efficient to implement and can be applied to verticals of different nature. We have successfully deployed the system to three different markets, and it handles multiple verticals in each market. The system is now serving hundreds of millions of queries live each day, and has improved user metrics considerably.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 AUnifiedSearchFederationSystemb	Yi Chang Luo Jie Sudarshan Lamkhede Rochit Sapra Evans Hsu Helen Song			A Unified Search Federation System based on Online User Feedback				10.1145/2487575.2488198		2013