基于信任计算和矩阵分解的推荐算法

基于矩阵分解的推荐算法普遍存在数据稀疏性、冷启动和抗攻击能力差等问题.针对上述问题,文中提出信任加强的矩阵分解推荐算法.首先,借鉴社会心理学中的信任产生原理,提出基于用户信誉度的信任扩展方法,缓解信任数据的稀疏性问题.然后,基于社交同质化原理,利用信任用户对评分矩阵分解过程中的用户潜在因子向量进行扩展,解决评分数据的稀疏性和新用户的冷启动问题.同时,利用信任关系对目标优化函数进行规格化约束,提高评分预测的准确性.基于通用测试数据集Epinions的实验表明,文中方法在推荐性能方面具有明显改善,可以有效解决数据稀疏性问题和冷启动问题.

Recommendation Algorithm Based on Trust Computation and Matrix Factorization

The recommendation algorithm based on matrix factorization has problems of data sparsity, cold start, poor anti-attack ability, etc. Therefore, a trust-based matrix factorization recommendation algorithm is proposed. Firstly, based on the principle of trust generation in social psychology, a reputation-based trust computation method is proposed to alleviate the trust data sparsity problem. Then, grounded on the principle of social homogenization, the user latent factor vector in the process of matrix factorization is extended by using the trust users to solve the rating data sparsity and new-user cold start problem. Meanwhile, social trust relationships are utilized to normalize the target function to improve the accuracy of the rating prediction. Experimental results on Epinions dataset show that the proposed method improves the recommendation precision greatly compared with the state-of-the-art methods, and it effectively solves the problems of data sparsity and cold start.