Chin. Phys. Lett.  2017, Vol. 34 Issue (6): 068902    DOI: 10.1088/0256-307X/34/6/068902
CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY |
User Heterogeneity and Individualized Recommender
Qing-Xian Wang1, Jun-Jie Zhang2, Xiao-Yu Shi2, Ming-Sheng Shang2**
1School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054
2Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714
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Qing-Xian Wang, Jun-Jie Zhang, Xiao-Yu Shi et al  2017 Chin. Phys. Lett. 34 068902
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Abstract Previous works on personalized recommendation mostly emphasize modeling peoples' diversity in potential favorites into a uniform recommender. However, these recommenders always ignore the heterogeneity of users at an individual level. In this study, we propose an individualized recommender that can satisfy every user with a customized parameter. Experimental results on four benchmark datasets demonstrate that the individualized recommender can significantly improve the accuracy of recommendation. The work highlights the importance of the user heterogeneity in recommender design.
Received: 03 March 2017      Published: 23 May 2017
PACS:  89.75.Hc (Networks and genealogical trees)  
  05.10.-a (Computational methods in statistical physics and nonlinear dynamics)  
  89.70.Cf (Entropy and other measures of information)  
Fund: Supported by the National Natural Science Foundation of China under Grant Nos 91646114, 61602434 and 61370150, and the Youth Innovation Promotion Association of Chinese Academy of Sciences under Grant No 2017393.
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https://cpl.iphy.ac.cn/10.1088/0256-307X/34/6/068902       OR      https://cpl.iphy.ac.cn/Y2017/V34/I6/068902
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Qing-Xian Wang
Jun-Jie Zhang
Xiao-Yu Shi
Ming-Sheng Shang
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