Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Similarity measure and instance selection for collaborative filtering
WWW '03 Proceedings of the 12th international conference on World Wide Web
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Knowledge and Information Systems
An automatic weighting scheme for collaborative filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
PocketLens: Toward a personal recommender system
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Cross-Domain Mediation in Collaborative Filtering
UM '07 Proceedings of the 11th international conference on User Modeling
Identifying Inter-Domain Similarities through Content-Based Analysis of Hierarchical Web-Directories
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Ways of computing diverse collaborative recommendations
AH'06 Proceedings of the 4th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Decentralized mediation of user models for a better personalization
AH'06 Proceedings of the 4th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Mediation of user models for enhanced personalization in recommender systems
User Modeling and User-Adapted Interaction
Multiagent system for learning objects retrieval with context attributes
International Journal of Computer Applications in Technology
Improved recommendations via (more) collaboration
Procceedings of the 13th International Workshop on the Web and Databases
Group-based recipe recommendations: analysis of data aggregation strategies
Proceedings of the fourth ACM conference on Recommender systems
A generic semantic-based framework for cross-domain recommendation
Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems
Shared collaborative filtering
Proceedings of the fifth ACM conference on Recommender systems
A PLA-based privacy-enhancing user modeling framework and its evaluation
User Modeling and User-Adapted Interaction
You are what you consume: a bayesian method for personalized recommendations
Proceedings of the 7th ACM conference on Recommender systems
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User data scarcity has always been indicated among the major problems of collaborative filtering recommender systems. That is, if two users do not share sufficiently large set of items for whom their ratings are known, then the user-to-user similarity computation is not reliable and a rating prediction for one user can not be based on the ratings of the other. This paper shows that this problem can be solved, and that the accuracy of collaborative recommendations can be improved by: a) partitioning the collaborative user data into specialized and distributed repositories, and b) aggregating information coming from these repositories. This paper explores a content-dependent partitioning of collaborative movie ratings, where the ratings are partitioned according to the genre of the movie and presents an evaluation of four aggregation approaches. The evaluation demonstrates that the aggregation improves the accuracy of a centralized system containing the same ratings and proves the feasibility and advantages of a distributed collaborative filtering scenario.