In a recent work [T. Zhou, Z. Kuscsik, J.-G. Liu, M. Medo, J.R. Wakeling, Y.-C. Zhang, Proc. Natl. Acad. Sci. 107 (2010) 4511], a personalized recommendation algorithm with high performance in both accuracy and diversity is proposed. This method is based on the hybridization of two single algorithms called probability spreading and heat conduction, which respectively are inclined to recommend popular and unpopular products. With a tunable parameter, an optimal balance between these two algorithms in system level is obtained. In this paper, we apply this hybrid method in individual level, namely each user has his/her own personalized hybrid parameter to adjust. Interestingly, we find that users are quite different in personalized hybrid parameters and the recommendation performance can be significantly improved if each user is assigned with his/her optimal personalized hybrid parameter. Furthermore, we find that users’ personalized parameters are negatively correlated with users’ degree but positively correlated with the average degree of the items collected by each user. With these understandings, we propose a strategy to assign users with suitable personalized parameters, which leads to a further improvement of the original hybrid method. Finally, our work highlights the importance of considering the heterogeneity of users in recommendation.
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机译:在最近的工作中[T. Zhou,Z.Kuscsik,J.-G. Liu M. Medo,J.R. Wakeling,Y.-C.张保Natl。学院科学[107(2010)4511],提出了一种在准确性和多样性上均具有高性能的个性化推荐算法。该方法基于两种分别称为概率扩展和热传导的算法的混合,这两种算法分别倾向于推荐受欢迎和不受欢迎的产品。通过可调参数,可以在系统级别上获得这两种算法之间的最佳平衡。在本文中,我们将这种混合方法应用于各个级别,即每个用户都有自己的个性化混合参数进行调整。有趣的是,我们发现用户的个性化混合参数完全不同,如果为每个用户分配了他/她的最佳个性化混合参数,则可以显着提高推荐性能。此外,我们发现用户的个性化参数与用户的程度呈负相关,但与每个用户收集的商品的平均程度呈正相关。基于这些理解,我们提出了一种为用户分配合适的个性化参数的策略,这将导致对原始混合方法的进一步改进。最后,我们的工作强调了在推荐中考虑用户异质性的重要性。
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