Fab: content-based, collaborative recommendation
Communications of the ACM
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
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Related, but not Relevant: Content-Based Collaborative Filtering in TREC-8
Information Retrieval
Collaborative Learning and Recommender Systems
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
An Improved Neighbor Selection Algorithm in Collaborative Filtering
IEICE - Transactions on Information and Systems
Collaborative filtering with the simple Bayesian classifier
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A user-item relevance model for log-based collaborative filtering
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
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The personalized recommendation system is required to save efforts in searching the items in ubiquitous commerce, it is very important for a recommendation system to predict accurately by analyzing user's preferences. A recommendation system utilizes in general an information filtering technique called collaborative filtering, which is based on the ratings matrix of other users who have similar preference. This paper proposes the user preference through Bayesian categorization for recommendation to overcome the sparsity problem and the first-rater problem of collaborative filtering. In addition, to determine the similarity between the users belonging to a particular class and new users, we assign different statistical values to the items that the users evaluated using Naive Bayesian classifier. We evaluated the proposed method on the EachMovie datasets of user ratings and it was found to significantly outperform the previously proposed method.