Document preprocessing for naive Bayes classification and clustering with mixture of multinomials
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Leveraging context in user-centric entity detection systems
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Improving Mobile Web-IR Using Access Concentration Sites in Search Results
WISE '08 Proceedings of the 9th international conference on Web Information Systems Engineering
Access concentration detection in click logs to improve mobile Web-IR
Information Sciences: an International Journal
Data Mining and Knowledge Discovery
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The exponential growth of the Web and the increasing ability of web search engines to index data have led to a problem of plenty. The number of results returned per query is typically in the order of millions of documents for many common queries. Although there is the benefit of added coverage for every query, the problem of ranking these documents and giving the best results gets worse. The problem is even more difficult in case of temporal and ambiguous queries. We try to address this problem using feedback from user query logs. We leverage a technology called Units for generating query refinements which are shown as Also try queries on Yahoo! Search. We consider these refinements as sub-concepts which help define user intent and use them to improve search relevance. The results obtained via live testing on Yahoo! Search are encouraging.