A Web Recommendation System Based on Maximum Entropy

We propose a Web recommendation system based on a maximum entropy model. Under the maximum entropy principle, we can combine multiple levels of knowledge about users’ navigational behavior in order to automatically generate the most effective recommendations for new users with similar profiles. The knowledge include the page-level statistics about users’ historically visited pages, and the aggregate usage patterns discovered through Web usage mining. In particular, we use a Web mining framework based on Probabilistic Latent Semantic Analysis to discover the underlying interests of Web users as well as temporal changes in these interests. Our experiments show that our recommendation system can achieve better accuracy when compared to standard approaches, while providing a better interpretation of Web users’ diverse navigational behavior.

• Type: Conference
• Year: 2005
• Where: ITCC
• Authors: Xin Jin, Bamshad Mobasher, Yanzan Zhou
• Keywords: Information Technology, ITCC 2005, Maximum Entropy, Navigational Behavior, Web Users