Bounded-SVD: A Matrix Factorization Method with Bound Constraints for Recommender Systems

Abstract

In this paper, we present a new matrix factorization method for recommender system problems, named bounded-SVD, which utilizes the constraint that all the ratings in the rating matrix are bounded within a pre-determined range. In our proposed method, the bound constraints are included in the objective function so that both the task of minimizing errors and the constraints are taken into account during the optimization process. For evaluation, we compare the performance of bounded-SVD with an existing method, called Bounded Matrix Factorization (BMF), which also uses the bound constraints on the ratings. The results on major real-world recommender system datasets show that our method outperforms BMF in almost cases and it is also faster and more simple to implement than BMF. Moreover, the way the bound constraints are integrated in bounded-SVD can also be applied to other optimization problems with bound constraints as well.