关键词: 协同过滤推荐, 冷启动, 数据稀疏, 动态标签, 信任关系, 矩阵概率分解模型

Abstract: This paper proposes a matrix probabilistic factorization model which integrates user dynamic tags and trust relationships to solve the problem of cold boot and data sparseness of collaborative filtering recommendation algorithm. Firstly, the model builds the dynamic contact of the user sets, tag sets and item sets, establishes user dynamic preference matrix. Secondly, it constructs user trust relationship matrix based on user social network information, the trust matrix uses the user trust feedback mechanism to update the trust value among users in real time. Finally, it integrates user dynamic tags and trust relationships, and carries out simulation experiments on MovieLens and Jester_Joke_data data sets. The results show that the proposed algorithm achieves better results in terms of absolute error mean, precision and recall rate, and effectively improves the performance of collaborative filtering recommendation algorithm to some extent.

Key words: collaborative filtering recommendation, cold boot, data sparseness, dynamic tags, trust relationships, matrix probabilistic factorization model