Abstract
Recommender systems are designed to effectively support individuals' decision-making process on various web sites. It can be naturally represented by a user-object bipartite network, where a link indicates that a user has collected an object. Recently, research on the information backbone has attracted researchers' interests, which is a sub-network with fewer nodes and links but carrying most of the relevant information. With the backbone, a system can generate satisfactory recommenda- tions while saving much computing resource. In this paper, we propose an enhanced topology-aware method to extract the information backbone in the bipartite network mainly based on the information of neighboring users and objects. Our backbone extraction method enables the recommender systems achieve more than 90% of the accuracy of the top-L recommendation, however, consuming only 20% links. The experimental results show that our method outperforms the alternative backbone extraction methods. Moreover, the structure of the information backbone is studied in detail. Finally, we highlight that the information backbone is one of the most important properties of the bipartite network, with which one can significantly improve the efficiency of the recommender system.
Highlights
Recommender systems are designed to effectively support individuals' decision-making process on various web sites
We propose an enhanced topology-aware method to extract the information backbone in the bipartite network mainly based on the information of neighboring users and objects
Our backbone extraction method enables the recommender systems achieve more than 90% of the accuracy of the top-L recommendation, consuming only 20% links
Summary
Recommender systems are designed to effectively support individuals' decision-making process on various web sites It can be naturally represented by a user-object bipartite network, where a link indicates that a user has collected an object. We propose an enhanced topology-aware method to extract the information backbone in the bipartite network mainly based on the information of neighboring users and objects. We highlight that the information backbone is one of the most important properties of the bipartite network, with which one can significantly improve the efficiency of the recommender system. The authors proposed time-aware and topology-aware link removal algorithms to extract the information backbone in a recommender system. In the user-object bipartite network, an information backbone is defined as a sub-network with a group of users, a set of objects and their links, which carries most of the relevant information for object recommendations. Few of them can be used in bipartite www.nature.com/scientificreports/
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