Mining Clickthrough Data for Collaborative Web Search
Web search can be regarded as a social behavior as users all over the world seek information from the Web by search engines. The purpose of this paper is to investigate the group behavior patterns of search activities based on the clickthrough data, to boost search performance. We proposed a Collaborative Web Search (CWS) framework based on the probabilistic modeling of the heterogeneous clickthrough objects, including users, queries and Web pages. The CWS framework consists of two steps: first, a cube-clustering approach is put forward to estimate the semantic cluster structures of the clickthrough data objects; next, Web search activities are conducted by leveraging the probabilistic relations among the cluster structures. Experiments on a real-world clickthrough data set validate the effectiveness of our CWS approach.
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