Web usage mining for Web site evaluation
Communications of the ACM
Automatic personalization based on Web usage mining
Communications of the ACM
Agglomerative clustering of a search engine query log
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WISEW '02 Proceedings of the Third International Conference on Web Information Systems Engineering (Workshops) - (WISEw'02)
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IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
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EWMF'05/KDO'05 Proceedings of the 2005 joint international conference on Semantics, Web and Mining
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Entropy-biased models for query representation on the click graph
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Characterizing commercial intent
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Image annotation using clickthrough data
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Cache-Based Query Processing for Search Engines
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AMT'12 Proceedings of the 8th international conference on Active Media Technology
Multimedia Tools and Applications
Annotation for free: video tagging by mining user search behavior
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Mining search and browse logs for web search: A Survey
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Image search by graph-based label propagation with image representation from DNN
Proceedings of the 21st ACM international conference on Multimedia
Personal and Ubiquitous Computing
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In this paper we present a new document representation model based on implicit user feedback obtained from search engine queries. The main objective of this model is to achieve better results in non-supervised tasks, such as clustering and labeling, through the incorporation of usage data obtained from search engine queries. This type of model allows us to discover the motivations of users when visiting a certain document. The terms used in queries can provide a better choice of features, from the user's point of view, for summarizing the Web pages that were clicked from these queries. In this work we extend and formalize as "query model" an existing but not very well known idea of "query view" for document representation. Furthermore, we create a novel model based on "frequent query patterns" called the "query-set model". Our evaluation shows that both "query-based" models outperform the vector-space model when used for clustering and labeling documents in a website. In our experiments, the query-set model reduces by more than 90% the number of features needed to represent a set of documents and improves by over 90% the quality of the results. We believe that this can be explained because our model chooses better features and provides more accurate labels according to the user's expectations.