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...

• Type: Conference
• Year: 2006
• Where: WWW
• Authors: Jian-Tao Sun, Xuanhui Wang, Dou Shen, Hua-Jun Zeng, Zheng Chen
• Keywords: Collaborative Web Search, Internet Technology, Real-world Clickthrough Data, Web Search Activities, WWW 2006