关键词: 推荐系统, 冷启动, 用户特征, 动态时间, 相似性计算

Abstract: Aiming at the problem of recommender system facing the data sparseness, cold-start, user features dynamic change and different users focus on different features, this paper puts forward a recommendation algorithm that combines user characteristics classification and the dynamic time. To compensate for the lack of computational similarity, it considers the dynamic change of user characteristics and classifies users based on feature, and solves the problem of cold start combining the user’s feature and the user’s score. The results show that the proposed algorithm can effectively improve the quality of recommendation.

Key words:  recommender system, cold-start, user characteristics, dynamic time, similarity calculation