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Mining clickthrough data for collaborative web search

12 years 7 months ago
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. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval-Search Process; H.3.5 [Information Storage and Retrieval]: Online Information Services-Web based services General Terms Algorithms, Experimentation, Performanc...
Jian-Tao Sun, Xuanhui Wang, Dou Shen, Hua-Jun Zeng
Added 22 Nov 2009
Updated 22 Nov 2009
Type Conference
Year 2006
Where WWW
Authors Jian-Tao Sun, Xuanhui Wang, Dou Shen, Hua-Jun Zeng, Zheng Chen
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