| CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY |
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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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| Cite this article: |
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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.
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Received: 03 March 2017
Published: 23 May 2017
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| PACS: |
89.75.Hc
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(Networks and genealogical trees)
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05.10.-a
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(Computational methods in statistical physics and nonlinear dynamics)
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89.70.Cf
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(Entropy and other measures of information)
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| 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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| [1] | Ekstrand M D, Riedl J T and Konstan J A 2011 Found. Trends Human-Comput. Interaction 4 175 | | [2] | Lü L, Medo M, Yeung C H, Zhang Y C, Zhang Z K and Zhou T 2012 Phys. Rep. 519 1 | | [3] | Pan X, Deng G S and Liu J G 2010 Chin. Phys. Lett. 27 068903 | | [4] | Van den Oord A, Dieleman S and Schrauwen B 2013 Adv. Neural Inf. Process. Syst. (Lake Tahoe Nevada USA 5–10 December 2013) p 2643 | | [5] | Resnick P, Iacovou N, Suchak M, Bergstrom P and Riedl J 1994 Proc. ACM Conf. Comput. Supported Cooperative Work (Chapel Hill NC USA 22–26 October 1994) p 175 | | [6] | Linden G, Smith B and York J 2003 IEEE Internet Computing 7 76 | | [7] | Bell R, Koren Y and Volinsky C Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Jose California, USA 12–15 August 2007) p 95 | | [8] | Deng S, Huang L and Xu G 2014 Expert Syst. Appl. 41 8075 | | [9] | Shang M S and Zhang Z K 2009 Chin. Phys. Lett. 26 118903 | | [10] | Zhou T, Kuscsik Z, Liu J G, Medo M, Wakeling J R and Zhang Y C 2010 Proc. Natl. Acad. Sci. USA 107 4511 | | [11] | Shang M S, Lü L, Zhang Y C and Zhou T 2010 Europhys. Lett. 90 48006 | | [12] | Chen L, de Gemmis M, Felfernig A, Lops P, Ricci F and Semeraro G 2013 ACM Trans. Interactive Intelligent Syst. 3 17 | | [13] | Ekstrand M D, Kluver D, Harper F M and Konstan J A Proceedings of the 9th ACM Conference on Recommender Systems (Vienna, Austria 16–20 September 2015) p 11 | | [14] | Shi X Y, Shang M S, Luo X, Khushnood A and Li J 2017 Physica A 467 490 | | [15] | Guan Y, Zhao D, Zeng A and Shang M S 2013 Physica A 392 3417 | | [16] | Shang M S 2016 J. Chin. West Normal University 1 61 (in Chinese) | | [17] | Lemire D and Maclachlan 2005 ASDM: Proceedings of the 2005 SIAM International Conference on Data Mining p 471 |
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