NDLI: Collaborative ranking: improving the relevance for tail queries

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Collaborative ranking: improving the relevance for tail queries

Content Provider	ACM Digital Library
Author	Zha, Hongyuan Zhou, Ke Li, Xin
Abstract	It is well known that tail queries contribute to a substantial fraction of distinct queries submitted to search engines and thus become a major battle field for search engines. Unfortunately, compared with popular queries, it is much more difficult to obtain good search results for tail queries due to the lack of important relevance signals, such as user clicks, phrase matches and so on. In this paper, we propose to utilize the similarities between different queries to overcome the data sparsity problem for tail queries. Specifically, we propose to jointly learn query similarities and the ranking function from data so that the relevance signals of different but related queries can be collaboratively pooled to enhance the ranking of tail queries. We emphasize that the joint optimization is critical so that the learned query similarity function can adapt to the problem of learning ranking functions. Our proposed method is evaluated on two data sets and the results show that our method improves the relevance of tail queries over several baseline alternatives.
Starting Page	1900
Ending Page	1904
Page Count	5
File Format	PDF
ISBN	9781450311564
DOI	10.1145/2396761.2398540
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-10-29
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Relevance Collaborative ranking Tail query Learning to rank Gradient boosting
Content Type	Text
Resource Type	Article

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