title: Extracting the information backbone in online system.
abstract: 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.
authors.affiliation: Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, PR China.
authors.forename: Qian-Ming
authors.initials: QM
authors.lastname: Zhang
authors.forename: An
authors.initials: A
authors.lastname: Zeng
authors.forename: Ming-Sheng
authors.initials: MS
authors.lastname: Shang
mesh_terms: D000465:Algorithms; D048088:Informatics / Q000379:methods*; D009862:Online Systems; D013997:Time Factors