Probabilistic Model Estimation for Collaborative Filtering Based on Items Attributes

With the development of e-commerce and the proliferation of easily accessible information, recommender systems have become a popular technique to prune large information spaces so that users are directed toward those items that best meet their needs and preferences. While many collaborative recommender systems (CRS) have succeeded in capturing the similarity among users or items based on ratings to provide good recommendation, there are still some challenges for them to be a more efficient RS. In this paper, we address three problems in CRS (user bias, non-transitive association, and new item problem) and provide a new item-based probabilistic model approach in order to solve the addressed problems in hopes of achieving better performance. In this probabilistic model, items are classified into groups and predictions are made for users considering the Gaussian distribution of user ratings. Experiments on a real-word data set illustrate that our proposed approach is comparable with others.