Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Communications of the ACM
Fab: content-based, collaborative recommendation
Communications of the ACM
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Collaborative filtering with privacy via factor analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Towards more conversational and collaborative recommender systems
Proceedings of the 8th international conference on Intelligent user interfaces
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Semantic Feedback for Hybrid Recommendations in Recommendz
EEE '05 Proceedings of the 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'05) on e-Technology, e-Commerce and e-Service
IEEE Transactions on Knowledge and Data Engineering
Artificial Intelligence Review
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A Hybrid, Multi-dimensional Recommender for Journal Articles in a Scientific Digital Library
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
A collaborative recommender system based on asymmetric user similarity
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
A recommender system based on multi-features
ICCSA'07 Proceedings of the 2007 international conference on Computational science and Its applications - Volume Part II
A distributed platform for personalized advertising in digital interactive TV environments
Journal of Systems and Software
A semantic social network-based expert recommender system
Applied Intelligence
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We describe a recommender system which uses a unique combination of content-based and collaborative methods to suggest items of interest to users, and also to learn and exploit item semantics. Recommender systems typically use techniques from collaborative filtering, in which proximity measures between users are formulated to generate recommendations, or content-based filtering, in which users are compared directly to items. Our approach uses similarity measures between users, but also directly measures the attributes of items that make them appealing to specific users. This can be used to directly make recommendations to users, but equally importantly it allows these recommendations to be justified. We introduce a method for predicting the preference of a user for a movie by estimating the user's attitude toward features with which other users have described that movie. We show that this method allows for accurate recommendations for a sub-population of users, but not for the entire user population. We describe a hybrid approach in which a user-specific recommendation mechanism is learned and experimentally evaluated. It appears that such a recommender system can achieve significant improvements in accuracy over alternative methods, while also retaining other advantages.