Mining clickthrough data for collaborative web search

This paper is to investigate the group behavior patterns of search activities based on Web search history data, i.e., clickthrough data, to boost search performance. We propose a Collaborative Web Search (CWS) framework based on the probabilistic modeling of the co-occurrence relationship among the heterogeneous web objects: users, queries, and Web pages. The CWS framework consists of two steps: (1) a cube-clustering approach is put forward to estimate the semantic cluster structures of the Web objects; (2) Web search activities are conducted by leveraging the probabilistic relations among the estimated cluster structures. Experiments on a real-world clickthrough data set validate the effectiveness of our CWS approach.

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In  WWW '06: Proceedings of the 15th international conference on World Wide Web

Publisher  ACM
Copyright is held by the author/owner. WWW 2006, May 22–26, 2006, Edinburgh, Scotland. ACM 1595933329/06/0005.

Details

TypeInproceedings
URLhttp://doi.acm.org/10.1145/1135777.1135958
Pages947–948
ISBN1-59593-323-9
AddressNew York, NY, USA
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