A low-order markov model integrating long-distance histories for collaborative recommender systems
Proceedings of the 14th international conference on Intelligent user interfaces
Combination of Web page recommender systems
Expert Systems with Applications: An International Journal
Predicting page occurrence in a click-stream data: statistical and rule-based approach
ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
NEWER: A system for NEuro-fuzzy WEb Recommendation
Applied Soft Computing
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In this paper, we propose a Web recommendation system based on a maximum entropy model. Under the maximum entropy principle, multiple sources of knowledge about users' navigational behavior in a Web site can be seamlessly combined to discover usage patterns and to automatically generate the most effective recommendations for new users with similar profiles. In this paper we integrate the knowledge from page-level clickstream statistics about users' past navigations with the aggregate usage patterns discovered through Web usage mining. Our experiment results show that our method can achieve better prediction accuracy when compared to standard recommendation approaches, while providing a better interpretation of Web users' diverse navigational behaviors.