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Regular Paper

Multi-Attribute Preferences Mining Method for Group Users with the Process of Noise Reduction

College of Economic and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Research Base for Civilian-Military Integration Development in Jiangsu Province, Nanjing 211106, China
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Traditional researches on user preferences mining mainly explore the user's overall preferences on the project, but ignore that the fundamental motivation of user preferences comes from their attitudes on some attributes of the project. In addition, traditional researches seldom consider the typical preferences combination of group users, which may have influence on the personalized service for group users. To solve this problem, a method with noise reduction for group user preferences mining is proposed, which focuses on mining the multi-attribute preference tendency of group users. Firstly, both the availability of data and the noise interference on preferences mining are considered in the algorithm design. In the process of generating group user preferences, a new path is used to generate preference keywords so as to reduce the noise interference. Secondly, the Gibbs sampling algorithm is used to estimate the parameters of the model. Finally, using the user comment data of several online shopping websites as experimental objects, the method is used to mine the multi-attribute preferences of different groups. The proposed method is compared with other methods from three aspects of predictive ability, preference mining ability and preference topic similarity. Experimental results show that the method is significantly better than other existing methods.

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Journal of Computer Science and Technology
Pages 944-960
Cite this article:
Tan Q-M, Wang X-N. Multi-Attribute Preferences Mining Method for Group Users with the Process of Noise Reduction. Journal of Computer Science and Technology, 2021, 36(4): 944-960.






Web of Science






Received: 15 October 2019
Accepted: 09 June 2021
Published: 05 July 2021
©Institute of Computing Technology, Chinese Academy of Sciences 2021