关键词: 推荐系统, 协同过滤, 用户偏好, 评分预测

Abstract: Collaborative filtering recommendation system suffers from series data sparsity problem. To solve the problem, this paper proposes a collaborative filtering recommendation method by combining rating preference and dual prediction. In the stage of calculating the nearest neighbors, to improve the calculation method of similarity, rating preference is introduced firstly. Then, in the stage of generating recommendation, a dual prediction method is proposed which is based on the user and the item nearest neighbors to predict the user preference more accurately. The experimental results on the MovieLens-1M data set indicate that the proposed method can relieve the influence of rating data sparsity on recommended results, significantly reduce the mean absolute error and effectively improve the recommendation precision.

Key words: recommendation system, collaborative filtering, user preference, rating prediction