GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Deterministic annealing EM algorithm
Neural Networks
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
An Efficient Boosting Algorithm for Combining Preferences
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Statistical Models for Co-occurrence Data
Statistical Models for Co-occurrence Data
Latent class models for collaborative filtering
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Maximum entropy for collaborative filtering
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
IEEE Transactions on Knowledge and Data Engineering
A study of mixture models for collaborative filtering
Information Retrieval
Automatic new topic identification using multiple linear regression
Information Processing and Management: an International Journal
Tensor-CUR decompositions for tensor-based data
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Short communication: Recommendation based on rational inferences in collaborative filtering
Knowledge-Based Systems
Aizu-BUS: need-based book recommendation using web reviews and web services
DNIS'07 Proceedings of the 5th international conference on Databases in networked information systems
Aggregating preference graphs for collaborative rating prediction
Proceedings of the fourth ACM conference on Recommender systems
Book recommendation system for utilisation of library services
International Journal of Computational Science and Engineering
Social and behavioural media access
SBNMA '11 Proceedings of the 2011 ACM workshop on Social and behavioural networked media access
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
TopRec: domain-specific recommendation through community topic mining in social network
Proceedings of the 22nd international conference on World Wide Web
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Collaborative filtering is a very useful general technique for exploiting the preference patterns of a group of users to predict the utility of items to a particular user. Previous research has studied several probabilistic graphic models for collaborative filtering with promising results. However, while these models have succeeded in capturing the similarity among users and items, none of them has considered the fact that users with similar interests in items can have very different rating patterns; some users tend to assign a higher rating to all items than other users. In this paper, we propose and study two new graphic models that address the distinction between user preferences and ratings. In one model, called the decoupled model, we introduce two different variables to decouple a user's preferences from hislher ratings. In the other, called the preference model, we model the orderings of items preferred by a user, rather than the user's numerical ratings of items. Empirical study over two datasets of movie ratings shows that, due to its appropriate modeling of the distinction between user preferences and ratings, the proposed decoupled model significantly outperforms all the five existing approaches that we compared with. The preference model, however, performs much worse than the decoupled model, suggesting that while explicit modeling of the underlying user preferences is very important for collaborative filtering, we can not afford ignoring the rating information completely.