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New Technology of Library and Information Service  2015, Vol. 31 Issue (9): 38-45    DOI: 10.11925/infotech.1003-3513.2015.09.06
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An Improved Collaborative Filtering Recommendation Algorithm with Indirect Trust Relationship
Wu Yingliang, Yao Huaidong, Li Cheng'an
E-Business Department, South China University of Technology, Guangzhou 510006, China
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[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.

Received: 04 January 2015      Published: 06 April 2016
:  TP301  

Cite this article:

Wu Yingliang, Yao Huaidong, Li Cheng'an. An Improved Collaborative Filtering Recommendation Algorithm with Indirect Trust Relationship. New Technology of Library and Information Service, 2015, 31(9): 38-45.

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