A Novel Session Clustering Approach Based On User Feed Back

Abstract

Most search engines (SE) adopt the keyword-based query method. The system would finally produce many complicated cross-field query outcomes when systems did not respectively manage to classify user latent query requisition and specifying fields. Web user session clustering is very important in web usage mining for web personalization. This paper proposes a novel session clustering approach based on user feedback. For every specific user, we get two semantic relationships between userquery and their search click information, such as expanded query contents and selected documents. In this way, user-session semantic similarity can be calculated using search click information, then user session can be clustered and disambiguated based on user's interests. Experiments have been conducted and the results have shown that our clustering approach is capable of clustering web user sessions with similar interests and topics.