论文部分内容阅读
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.