GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th 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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Collaborative filtering is the process of making recommendations regarding the potential preference of a user, for example shopping on the Internet, based on the preference ratings of the user and a number of other users for various items. This paper considers collaborative filtering based on explicit multivalued ratings. To evaluate the algorithms, we consider only pure collaborative filtering, using ratings exclusively, and no other information about the people or items. Our approach is to predict a user's preferences regarding a particular item by using other people who rated that item and other items rated by the user as noisy sensors. The noisy sensor model uses Bayes' theorem to compute the probability distribution for the user's rating of a new item. We give two variant models: in one, we learn a classical normal linear regression model of how users rate items; in another, we assume different users rate items the same, but the accuracy of the sensors needs to be learned. We compare these variant models with state-of-the-art techniques and show how they are significantly better, whether a user has rated only two items or many. We report empirical results using the EachMovie database of movie ratings. We also show that by considering items similarity along with the users similarity, the accuracy of the prediction increases.