Agents that reduce work and information overload
Communications of the ACM
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Personal ontologies for web navigation
Proceedings of the ninth international conference on Information and knowledge management
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
A Movie Recommendation System—An Application of Voting Theory in User Modeling
User Modeling and User-Adapted Interaction
A Personalized Music Filtering System Based on Melody Style Classification
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Content-Based Filtering System for Music Data
SAINT-W '04 Proceedings of the 2004 Symposium on Applications and the Internet-Workshops (SAINT 2004 Workshops)
An intelligent news recommender agent for filtering and categorizing large volumes of text corpus
International Journal of Intelligent Systems
A music recommendation system based on music and user grouping
Journal of Intelligent Information Systems - Special issue: Intelligent multimedia applications
A probabilistic model for music recommendation considering audio features
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
A human-oriented image retrieval system using interactive genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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The authors describe a recommender model that uses intermediate agents to evaluate a large body of subjective data according to a set of rules and make recommendations to users. After scoring recommended items, agents adapt their own selection rules via interactive evolutionary computing to fit user tastes, even when user preferences undergo a rapid change. The model can be applied to such tasks as critiquing large numbers of music or written compositions. In this paper we use musical selections to illustrate how agents make recommendations and report the results of several experiments designed to test the model's ability to adapt to rapidly changing conditions yet still make appropriate decisions and recommendations.