  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.
