关键词: 推荐系统, 协同过滤, 概率矩阵分解, 正则化参数

Abstract: Regularization technique is needed for the collaborative filtering recommendation algorithm based on the gradient descent matrix factorization to restrict the problems. Regularization parameter used in the loss function can control the trade-off between prediction accuracy of the model and overfitting avoidance to the training. A method of multiple regularization parameters is proposed. It can obtain the regularization parameters according to the user activity and item popularity, avoid the overfitting in the users and items with different numbers, and get better prediction accuracy. The experiment results show that this method is correct and feasible.

Key words: recommender system, collaborative filtering, probabilistic matrix factorization, regularization parameter