中国科学院重庆绿色智能技术研究院机构知识库

QQ客服 官方微博 反馈留言

	Effects of the bipartite structure of a network on performance of recommenders
	Wang, Qing-Xian1; Li, Jian2; Luo, Xin2; Xu, Jian-Jun2; Shang, Ming-Sheng2
	2018-02-15
摘要	Recommender systems aim to predict people's preferences for online items by analyzing their historical behaviors. A recommender can be modeled as a high-dimensional and sparse bipartite network, where the key issue is to understand the relation between the network structure and a recommender's performance. To address this issue, we choose three network characteristics, clustering coefficient, network density and user-item ratio, as the analyzing targets. For the cluster coefficient, we adopt the Degree-preserving rewiring algorithm to obtain a series of bipartite network with varying cluster coefficient, while the degree of user and item keep unchanged. Furthermore, five state-of-the-art recommenders are applied on two real datasets. The performances of recommenders are measured by both numerical and physical metrics. These results show that a recommender's performance is positively related to the clustering coefficient of a bipartite network. Meanwhile, higher density of a bipartite network can provide more accurate but less diverse or novel recommendations. Furthermore, the user-item ratio is positively correlated with the accuracy metrics but negatively correlated with the diverse and novel metrics. (C) 2017 Elsevier B.V. All rights reserved.
关键词	Bipartite network Clustering coefficient Network density User-item ratio Recommender system
DOI	10.1016/j.physa.2017.11.053
发表期刊	PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
ISSN	0378-4371
卷号	492页码:1257-1266
收录类别	SCI
WOS记录号	WOS:000423495100104
语种	英语