关键词: 矩阵分解, 单类协同过滤, 稀疏性, 正负样本

Abstract: The sparsity of data and the lack of negative samples have a bad influence on the result of matrix factorization based one-class collaborative filtering approach, such as features extracted are not obvious and low differentiation between training results. This paper proposes an algorithm to choose positive and negative samples by consi-dering both item similarity and user activity. With this algorithm, more positive samples can be added via the item similarity and more negative samples can be added with the aid of the user activity. In this case, when matrix factorization is used in one-class collaborative filering, the influence of the sparsity and the lack of negative samples can be effectively reduced. A series of experiments show that the new algorithm can add positive and negative samples more accurately, reduce the sparsity, and improve the recommendation accuracy.

Key words: matrix factorization, one-class collaborative filtering, sparsity, positive and negative samples