[Objective] In traditional collaborative filtering algorithms, the issues such as data sparsity may make the quality of recommendation worse. This paper attempts to solve it by optimizing the recommendation mechanisms. [Methods] This paper uses cohesive subgroup analysis techniques to identify indirect trust relationship in trust networks, and combines with direct trust relationship to generate an integrated trust, which is used to calculate the user similarity in the new collaborative filtering recommendation algorithm. [Results] Experimental results show that the ultimate trust combining 35% direct and 65% indirect relationship can improve the accuracy of CF algorithms, and compared with only using direct trust relationship, the indirect trust relationship could not be ignored. [Limitations] When considering the indirect trust in the trust network, this paper ignores the impact of more intermediate nodes between two users. [Conclusions] Soft integration of indirect trust relationship can improve the recommendation accuracy of collaborative filtering algorithms.
吴应良, 姚怀栋, 李成安. 一种引入间接信任关系的改进协同过滤推荐算法[J]. 现代图书情报技术, 2015, 31(9): 38-45.
Wu Yingliang, Yao Huaidong, Li Cheng'an. An Improved Collaborative Filtering Recommendation Algorithm with Indirect Trust Relationship. New Technology of Library and Information Service, DOI：10.11925/infotech.1003-3513.2015.09.06.
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