Fab: content-based, collaborative recommendation
Communications of the ACM
Evaluation of search results: a new approach
Journal of the American Society for Information Science
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Text Information Retrieval Systems
Text Information Retrieval Systems
Information Retrieval
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Letizia: an agent that assists web browsing
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
ROSA: multi-agent system for web services personalization
AWIC'03 Proceedings of the 1st international Atlantic web intelligence conference on Advances in web intelligence
Adaptrank: a hybrid method for improving recommendation recall
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Non-textual document ranking using crawler information and web usage mining
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
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In this paper a fuzzy-based recommendation method is presented. Its main goal is to improve the recommendation recall maintaining high recommendation precision. The formal model has been built to describe the method and to analyze how the measures used in traditional Information Retrieval may be adapted to evaluate the effectiveness of recommendation process. The original contributions consist among others of proving several properties which show that the method is able to adapt to changing user's needs and achieving the maximum effectiveness if the component methods work properly.