Zhu Xuzhen, Yang Yujie, Chen Guilin, Medo Matus, Tian Hui, Cai Shi-Min
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
Department of Physics, University of Fribourg, Chemin du Musée 3, CH-1700 Fribourg, Switzerland.
PLoS One. 2017 Jul 27;12(7):e0181402. doi: 10.1371/journal.pone.0181402. eCollection 2017.
Methods used in information filtering and recommendation often rely on quantifying the similarity between objects or users. The used similarity metrics often suffer from similarity redundancies arising from correlations between objects' attributes. Based on an unweighted undirected object-user bipartite network, we propose a Corrected Redundancy-Eliminating similarity index (CRE) which is based on a spreading process on the network. Extensive experiments on three benchmark data sets-Movilens, Netflix and Amazon-show that when used in recommendation, the CRE yields significant improvements in terms of recommendation accuracy and diversity. A detailed analysis is presented to unveil the origins of the observed differences between the CRE and mainstream similarity indices.
信息过滤和推荐中使用的方法通常依赖于量化对象或用户之间的相似度。所使用的相似度度量常常受到对象属性之间相关性所产生的相似度冗余问题的困扰。基于一个无加权无向对象-用户二分网络，我们提出了一种基于网络上传播过程的修正冗余消除相似度指数（CRE）。在三个基准数据集——Movilens、Netflix和亚马逊上进行的大量实验表明，在推荐中使用时，CRE在推荐准确性和多样性方面产生了显著提升。我们进行了详细分析，以揭示CRE与主流相似度指数之间观察到的差异的根源。
