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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (7): 90-99    DOI: 10.11925/infotech.2096-3467.2017.07.11
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Improving Collaborative Filtering Recommendation Based on Trust Relationship Among Users
Fuliang Xue(),Junling Liu
Business School, Tianjin University of Finance & Economics, Tianjin 300222, China
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[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.

Key wordsE-commerce Recommendation      User Trust      Collaborative Filtering      Cold Start      Sparsity     
Received: 26 May 2017      Published: 13 September 2017

Cite this article:

Fuliang Xue,Junling Liu. Improving Collaborative Filtering Recommendation Based on Trust Relationship Among Users. Data Analysis and Knowledge Discovery, 2017, 1(7): 90-99.

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