User preference through Bayesian categorization for recommendation

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.