Search engine query log is a valuable information source to analyze the users' interests and preferences. In existing work, click graph is intensively utilized to analyze the information in query log. However, click graph is usually plagued by low information coverage, failure of capturing the diverse types of co-occurrence and the incapability of discovering the latent semantics in data. In this paper, we go beyond click graph and analyze query log through the new perspective of probabilistic topic modeling. In order to systematically explore the potential assumptions of the latent structure of the log data, we propose three different topic models. The first model, the Meta-word Model (MWM), unifies the co-occurrence of query terms and URLs by the meta-word occurrence. The second model, the Term-URL Model (TUM), captures the characteristics of query terms and URLs separately. The third model, the Clickthrough Model (CTM), captures the clicking behavior explicitly and models the ternary relation between search queries, query terms and URLs. We evaluate the three proposed models against several strong baselines on a real-life query log. The experimental results show that the proposed models demonstrate significantly improved performance with respect to different quantitative metrics and also in applications such as date prediction, community discovery and URL annotation.
展开▼
