结合信任关系的用户聚类协同过滤推荐算法

User Clustering Collaborative Filtering Recommendation Algorithm Combined with Trust Relationship

在传统的协同过滤推荐算法中,相似度计算是算法中的核心,然而之前的计算方式过于依赖用户的评分,没有考虑到用户本身的属性以及信任度,并且没有对恶意用户进行区分,为解决上诉问题,本文将一种改进的新型信任关系度量方式融入到相似度计算中,这种新型的方法不仅考虑了恶意用户的影响,并且有效地结合用户本身的属性.另外,文章就热点问题对相似度计算也进行了改进.算法最终利用初始用户聚类不断迭代得到相邻用户,有效的消除了冷启动和数据稀疏的问题.实验部分,通过与其它几种推荐算法的比较可以证明,提出的算法能够有效提升推荐准确度.

In the traditional collaborative filtering recommendation algorithm,similarity calculation is the core of the algorithm.However,the previous calculation method is too dependent on the user’s score,does not consider the user’s own attributes and trust relationship,and does not distinguish malicious users.In order to solve the appeal problem,this study introduces an improved new trust relationship measurement method into similarity calculation.This new method not only considers the influence of malicious users,but also combines the properties of users effectively.In addition,the study also improves the similarity algorithm on the hot issues.The algorithm finally uses the initial user clustering to get the adjacent users,effectively eliminating the cold start and data sparsity.In the experimental part,it can be proved that the proposed algorithm can effectively improve the recommendation accuracy by comparing with other algorithms.