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 mainly dedicated to improve the recommendation performance (accuracy and diversity) of the algorithms while 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 improve both of their effectiveness and efficiency.

References in corpus (5)

• Recommender Systems
• Extracting the multiscale backbone of complex weighted networks
• Synchronization is optimal in non-diagonalizable networks
• Heat Conduction Process on Community Networks as a Recommendation Model
• Scale-free trees: the skeletons of complex networks

Cited by in corpus (12)

• Ranking users, papers and authors in online scientific communities
• Group-based ranking method for online rating systems with spamming attacks
• A vertex similarity index for better personalized recommendation
• Information filtering via hybridization of similarity preferential diffusion processes
• Promoting cold-start items in recommender systems
• Strong ties promote the epidemic prevalence in susceptible-infected-susceptible spreading dynamics
• Fast extraction of the backbone of projected bipartite networks to aid community detection
• Information filtering in sparse online systems: recommendation via semi-local diffusion
• Improving the performance of reputation evaluating by combining the structure of network and nonlinear recovery
• An Investigation on Social Network Recommender Systems and Collaborative Filtering Techniques
• Whether Information Network Supplements Friendship Network
• Network Backboning with Noisy Data