A Multi-attribute Collaborative Filtering Recommendation Algorithm Based on Improved Group Decision-Making

Abstract : 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 the 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.
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Communication dans un congrès
Kecheng Liu; Stephen R. Gulliver; Weizi Li; Changrui Yu. 15th International Conference on Informatics and Semiotics in Organisations (ICISO), May 2014, Shanghai, China. Springer, IFIP Advances in Information and Communication Technology, AICT-426, pp.320-330, 2014, Service Science and Knowledge Innovation. 〈10.1007/978-3-642-55355-4_33〉
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Changrui Yu, Yan Luo, Kecheng Liu. A Multi-attribute Collaborative Filtering Recommendation Algorithm Based on Improved Group Decision-Making. Kecheng Liu; Stephen R. Gulliver; Weizi Li; Changrui Yu. 15th International Conference on Informatics and Semiotics in Organisations (ICISO), May 2014, Shanghai, China. Springer, IFIP Advances in Information and Communication Technology, AICT-426, pp.320-330, 2014, Service Science and Knowledge Innovation. 〈10.1007/978-3-642-55355-4_33〉. 〈hal-01350939〉

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