基于用户间信任关系改进的协同过滤推荐方法

Improving Collaborative Filtering Recommendation Based on Trust Relationship Among Users

【目的】利用用户间信任关系改进协同过滤推荐中用户相似性计算精度,即在目标用户没有相似用户的前提下,从其信任用户中选择信任值高的作为相似用户,进而提高相似用户聚类效果,提高推荐质量,并有效缓解协同过滤推荐稀疏性和冷启动问题。【方法】筛选信任用户作为相似用户;根据选择的信任用户和目标用户形成一个项目的评分集,并对目标用户未评价过的项目进行评分估算(根据信任用户评分进行简单的评分计算);将用户间的信任关系依据方差大小进行量化,形成一个调节因子。本文的创新点就在于调节因子的计算,并将调节因子纳入用户相似性计算,形成相似性用户聚类簇,在此基础上在相似用户之间进行交叉推荐。【结果】通过平均绝对误差指标进行实验评价,结果表明基于信任关系的协同过滤推荐方法相比传统协同过滤,在推荐精度上更加准确,并同时有效缓解了冷启动和稀疏性问题。【局限】本文提出的方法仅在具有信任关系的一个算例上进行实验测试,需在其他数据集和真实应用场景下进一步检验。【结论】用户间信任关系蕴涵非常有价值的信息,对用户信任关系进行量化,并纳入用户相似性计算,在此基础上实施协同过滤推荐,对缓解冷启动与稀疏性问题具有较好的理论和实践意义。

[Objective] This paper tries to improve user similarity calculation in collaborative filtering recommendation with trust relationship among them. Once there is no similar user for members of the target group, we recommend the most trusted ones as the similar users. [Methods] First, we retrieved the trusted users as candidates for the similar users. Second, we combined the trusted and the target users to form a project score set, and evaluated the estimated value of the projects receiving no comment from the target group. Third, we quantified the trust relationship among users to form a regulation factor. Finally, we calculated the adjustment factor and created the similarity cluster of users, and made cross-recommendation among similar users. [Results] The collaborative filtering recommendation method based on trust relationship had better performance than traditional ones. [Limitations] Only examined the new method with one sample dataset with trusted relationship. More research is needed to test the proposed method with other datasets. [Conclusions] The trusted relationship among users contains valuable information, which could be used to calculate user similarity for collaborative filtering recommendation services, and then effectively solves the sparsity and cold start issue.