Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Communications of the ACM
Fab: content-based, collaborative recommendation
Communications of the ACM
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
The role of transparency in recommender systems
CHI '02 Extended Abstracts on Human Factors in Computing Systems
Methods and metrics for cold-start recommendations
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
User Modeling for Adaptive News Access
User Modeling and User-Adapted Interaction
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
The Journal of Machine Learning Research
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Explanation in Recommender Systems
Artificial Intelligence Review
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Matrix factorization and neighbor based algorithms for the netflix prize problem
Proceedings of the 2008 ACM conference on Recommender systems
A Survey of Explanations in Recommender Systems
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Content-based recommendation systems
The adaptive web
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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We propose a new hybrid recommender system that combines some advantages of collaborative and content-based recommender systems. While it uses ratings data of all users, as do collaborative recommender systems, it is also able to recommend new items and provide an explanation of its recommendations, as do content-based systems. Our approach is based on the idea that there are communities of users that find the same characteristics important to like or dislike a product. This model is an extension of the probabilistic latent semantic model for collaborative filtering with ideas based on clusterwise linear regression. On a movie data set, we show that the model, at the cost of a very small loss in overall performance, is able to recommend new items and give an explanation of its recommendations to its users.