document: 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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