Mining clickthrough data for collaborative web search

Jian-Tao Sun, Xuanhui Wang, Dou Shen, Hua-Jun Zeng, and Zheng Chen

Abstract

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.

Details

Publication typeInproceedings
Published inWWW '06: Proceedings of the 15th international conference on World Wide Web
URLhttp://doi.acm.org/10.1145/1135777.1135958
Pages947–948
ISBN1-59593-323-9
AddressNew York, NY, USA
PublisherACM
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