融合信任传播和奇异值分解的社会化推荐算法

Social Recommendation Algorithm Integrating Trust Propagation and Singular Value Decomposition

针对用户信任矩阵中的数据稀疏问题,设计用户信任关系的传播规则,根据该规则计算用户之间的信任度,填充用户信任矩阵。在此基础上,结合用户信任传播算法和奇异值分解模型,提出一种社会化推荐算法,将用户评分矩阵与信任关系矩阵相结合,提高推荐系统的预测准确率。在Epinions和Filmtrust公开数据集上的实验结果表明,该算法相比传统推荐算法具有更高的推荐质量。

Aiming at the data sparsity of user trust matrix, this paper designs a propagation rule for trust relationships among users. It computes the trust degree of user according to the rule, and then uses the trust degree to fill the user trust matrix. It proposes a social recommendation algorithm based on users＇ trust propagation algorithm and Singular Value Decomposition（SVD） model, The user scoring matrix is combined with the trust relation matrix to improve The prediction accuracy of the recommended system. Experimental results on both Epinions and Filmtrust publicly available datasets show that compared with the traditional recommendation algorithm, the proposed algorithm has higher recommendation quality.