Description

As the competition of Web search market increases, there is a high demand for personalized Web search to conduct retrieval incorporating Web users’ information needs. This paper focuses on utilizing click through data to improve Web search. Since millions of searches are conducted every day , a search engine accumulates a large volume of click through data, which records who submits queries and which pages he/she clicks on. The click through data is highly sparse and contains different types of objects (user, query and Web page), and the relationships among these objects are also very complicated. By performing analysis on these data, we attempt to discover Web users’ interests and the patterns that users locate information. In this paper, a novel approach WebClickSVD is proposed to improve Web search. The clickthrough data is represented by a 3-order tensor, on which we perform 3-mode analysis using the higher-order singular value decomposition technique to automatically capture the latent factors that govern the relations among these multi-type objects: users, queries and Web pages. A tensor reconstructed based on the WebClickSVD analysis reflects both the observed interactions among these objects and the implicit associations among them. Therefore, Web search activities can be carried out based on WebClickSVD analysis. Experimental evaluations using a real-world data set collected from an MSN search engine show that WebClickSVD achieves encouraging search results in comparison with some standard methods.