Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, PR China.
PLoS One. 2013 May 14;8(5):e62624. doi: 10.1371/journal.pone.0062624. Print 2013.
Information overload is a serious problem in modern society and many solutions such as recommender system have been proposed to filter out irrelevant information. In the literature, researchers have been mainly dedicated to improving the recommendation performance (accuracy and diversity) of the algorithms while they have overlooked the influence of topology of the online user-object bipartite networks. In this paper, we find that some information provided by the bipartite networks is not only redundant but also misleading. With such "less can be more" feature, we design some algorithms to improve the recommendation performance by eliminating some links from the original networks. Moreover, we propose a hybrid method combining the time-aware and topology-aware link removal algorithms to extract the backbone which contains the essential information for the recommender systems. From the practical point of view, our method can improve the performance and reduce the computational time of the recommendation system, thus improving both of their effectiveness and efficiency.
信息过载是现代社会的一个严重问题，已经提出了许多解决方案，如推荐系统，以过滤掉不相关的信息。在文献中，研究人员主要致力于提高算法的推荐性能（准确性和多样性），而忽略了在线用户-对象二分网络拓扑结构的影响。在本文中，我们发现二分网络提供的一些信息不仅是冗余的，而且具有误导性。具有这种“少即是多”的特点，我们设计了一些算法通过从原始网络中删除一些链接来提高推荐性能。此外，我们提出了一种结合时间感知和拓扑感知链路删除算法的混合方法，以提取包含推荐系统基本信息的骨干网络。从实际的角度来看，我们的方法可以提高推荐系统的性能并减少计算时间，从而提高其有效性和效率。
