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.

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