Information Filtering via Collaborative User Clustering Modeling
This work addresses the need for more accurate personalized recommendations in information filtering systems, but it is incremental as it builds on standard Matrix Factorization.
The paper tackles the problem of improving recommendation accuracy in collaborative filtering by integrating user clustering regularization into Matrix Factorization, resulting in significantly better performance than baseline methods on the MovieLens dataset.
The past few years have witnessed the great success of recommender systems, which can significantly help users find out personalized items for them from the information era. One of the most widely applied recommendation methods is the Matrix Factorization (MF). However, most of researches on this topic have focused on mining the direct relationships between users and items. In this paper, we optimize the standard MF by integrating the user clustering regularization term. Our model considers not only the user-item rating information, but also takes into account the user interest. We compared the proposed model with three typical other methods: User-Mean (UM), Item-Mean (IM) and standard MF. Experimental results on a real-world dataset, \emph{MovieLens}, show that our method performs much better than other three methods in the accuracy of recommendation.