A New Approach of Matrix Factorization and Its Application in Recommender Systems

Matrix factorization (MF) is a major technique for collaborative filtering of recommender systems. However, in the traditional MF model, it is difficult to tune the regularization parameter, and the predicted ratings may not lie within the given range. In this paper, we propose a new MF approach, in which MF is modeled as a constrained optimization problem and the constraint conditions are given in terms of the range of the factorization matrices. Under the new model, the regularization parameter is not needed and the predicted ratings are limited in the given range. We further provide a feasible direction method to solve the new model. Experimental results demonstrate that our approach outperforms the traditional MF.

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