Extracting the information backbone in online system

TL;DR

This paper introduces a method to improve recommender systems by removing redundant and misleading links from user-object bipartite networks, enhancing performance and efficiency.

Contribution

It proposes a hybrid link removal algorithm that extracts the network backbone, focusing on essential information for better recommendations.

Findings

Improved recommendation accuracy and diversity.

Reduced computational time for recommendation algorithms.

Enhanced effectiveness and efficiency of recommender systems.

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.