Multi-attribute Collaborative Filtering Recommendation based on Improved Group Decision-making

  • Changrui Yu School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, China
  • Luo Yan The Sydney Institute of Language and Commerce, Shanghai University, 20 Chengzhong RD, Shanghai 201800, China
  • Liu Kecheng Informatics Research Centre, Henley Business School, University of Reading, Reading, RG6 3XA, U.K.


Currently researchers in the field of personalized recommendations bear little consideration on users' interest differences in resource attributes although resource attribute is usually one of the most important factors in determining user preferences. To solve this problem, the paper builds an evaluation model of user interest based on resource multi-attributes, proposes a modified Pearson-Compatibility multi-attribute group decision-making algorithm, and introduces an algorithm to solve the recommendation problem of k-neighbor similar users. Considering the characteristics of collaborative filtering recommendation, the paper addresses the issues on the preference differences of similar users, incomplete values, and advanced converge of the algorithm. Thus the paper realizes multi-attribute collaborative filtering. Finally, the effectiveness of the algorithm is proved by an experiment of collaborative recommendation among multi-users based on virtual environment. The experimental results show that the algorithm has a high accuracy on predicting target users' attribute preferences and has a strong anti-interference ability on deviation and incomplete values.


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How to Cite
YU, Changrui; YAN, Luo; KECHENG, Liu. Multi-attribute Collaborative Filtering Recommendation based on Improved Group Decision-making. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 10, n. 5, p. 746-759, july 2015. ISSN 1841-9844. Available at: <>. Date accessed: 04 dec. 2020. doi:


personalized recommendation; Pearson-Compatibility; group decision-making; multi-attribute; collaborative filtering