User Heterogeneity and Individualized Recommender
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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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Qing-Xian Wang, Jun-Jie Zhang, Xiao-Yu Shi, Ming-Sheng Shang. User Heterogeneity and Individualized Recommender[J]. Chin. Phys. Lett., 2017, 34(6): 068902. DOI: 10.1088/0256-307X/34/6/068902
Qing-Xian Wang, Jun-Jie Zhang, Xiao-Yu Shi, Ming-Sheng Shang. User Heterogeneity and Individualized Recommender[J]. Chin. Phys. Lett., 2017, 34(6): 068902. DOI: 10.1088/0256-307X/34/6/068902
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Qing-Xian Wang, Jun-Jie Zhang, Xiao-Yu Shi, Ming-Sheng Shang. User Heterogeneity and Individualized Recommender[J]. Chin. Phys. Lett., 2017, 34(6): 068902. DOI: 10.1088/0256-307X/34/6/068902
Qing-Xian Wang, Jun-Jie Zhang, Xiao-Yu Shi, Ming-Sheng Shang. User Heterogeneity and Individualized Recommender[J]. Chin. Phys. Lett., 2017, 34(6): 068902. DOI: 10.1088/0256-307X/34/6/068902
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