A Feature-Based Co-Clustering Model

Recommendation system can effectively solve the personalized recommendation problem for users. As one of the most commonly used algorithm in recommendation system, collaborative filtering needs to take all the items into account, while a specific user may be only interested in the items in some certain domains. It’s more natural to make recommendation for a user via the correlated domains than the entire items, therefore, users and items can be grouped according to their interests or characteristics, and then the recommendations can be made with the user-item subgroups. Based on this idea, we propose a novel co-clustering method based on the features of users and items to find the meaningful subgroups. The proposed method includes two main modules: a feature representation model to explore the interests of the users and the characteristics of the items, and a graph model constructed in accordance with these features for coming up with the final clustering results which are used for making recommendation. In this paper, we introduce the framework of our method and give an effective solution to get the features and the clustering results. Finally, by comparing with a variety of newest algorithms on three open datasets, we verify that the proposed method can significantly improve the accuracy of recommender system.