Previous works on personalized recommendation mostly emphasize modeling peoples＇ diversity in potential fa- vorites into a uniform recommender. However, these recommenders always ignore the heterogeneity of users at ...Previous works on personalized recommendation mostly emphasize modeling peoples＇ diversity in potential fa- vorites 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.展开更多
基金Supported by the National Natural Science Foundation of China under Grant Nos 91646114,61602434 and 61370150the Youth Innovation Promotion Association of Chinese Academy of Sciences under Grant No 2017393
文摘Previous works on personalized recommendation mostly emphasize modeling peoples＇ diversity in potential fa- vorites 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.
