Extracting the information backbone in online system
This work addresses information overload for users of online systems by incrementally enhancing recommender systems through network topology optimization.
The paper tackles the problem of information overload in online systems by identifying that some links in user-object bipartite networks are redundant and misleading, and proposes a hybrid method to remove such links, improving recommendation performance and reducing computational time.
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.