Uncovering the essential links in online commercial networks.
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.
Trusted Computing and Automated Reasoning Lab, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China.|Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China.|Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China.
Wei
W
Zeng
Trusted Computing and Automated Reasoning Lab, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China.|Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China.|Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China.
Meiling
M
Fang
Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China.|Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China.
Junming
J
Shao
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, P. R. China.
Mingsheng
M
Shang
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