An initialization method to improve the training time of matrix factorization algorithm for fast recommendation

Abstract

Recommendation systems are successful personalizing tools and information filtering in web. One of the most important recommendation methods is matrix factorization method. In matrix factorization method, the latent features of users and items are determined in such a way that the inner product of the latent features of a user with the latent features of an item is equal to that user's rating on that item. This model is solved using alternate optimization algorithm. The solution and the prediction error of this algorithm depend on the initial values of the latent features of users which are usually set to small random values. The purpose of this paper is to propose a fast alternate optimization algorithm for matrix factorization which converges to a good solution. To do so, firstly, we show experimentally that if the latent feature vector of each user is initialized by a vector of which elements are equal, we can also obtain a proper solution using the alternate optimization algorithm. Then, we prove that if our proposed initialization method is used, the alternate optimization algorithm for matrix factorization can be simplified using Sherman–Morrison formula. Experimental results on 5 real datasets show that the runtime of our proposed algorithm is 2–45 times less than the traditional method.