Patterns of search: analyzing and modeling Web query refinement
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IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic query expansion using query logs
Proceedings of the 11th international conference on World Wide Web
Exploiting query history for document ranking in interactive information retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Mining anchor text for query refinement
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Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
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WISE '08 Proceedings of the 9th international conference on Web Information Systems Engineering
The Metadata Triumvirate: Social Annotations, Anchor Texts and Search Queries
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
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Dynamic refinement of search engines results utilizing the user intervention
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APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Incorporating social anchors for ad hoc retrieval
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
Multimedia search reranking: A literature survey
ACM Computing Surveys (CSUR)
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This work addresses two common problems in search, frequently occurring with underspecified user queries: the top-ranked results for such queries may not contain documents relevant to the user's search intent, and fresh and relevant pages may not get high ranks for an underspecified query due to their freshness and to the large number of pages that match the query, despite the fact that a large number of users have searched for parts of their content recently. We propose a novel method, Q-Rank, to effectively refine the ranking of search results for any given query by constructing the query context from search query logs. Evaluation results show that Q-Rank gains a considerable advantage over the current ranking system of a large-scale commercial Web search engine, being able to improve the relevance of search results for 82% of the queries.