中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 重庆绿色智能技术研究院
  3. 中国科学院重庆绿色智能技术研究院
  4. 大数据挖掘及应用中心
Effects of the bipartite structure of a network on performance of recommenders

文献类型：期刊论文

作者Wang, Qing-Xian1; Li, Jian2; Luo, Xin2; Xu, Jian-Jun2; Shang, Ming-Sheng2
刊名PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
出版日期2018-02-15
卷号492页码:1257-1266
关键词Bipartite network Clustering coefficient Network density User-item ratio Recommender system
ISSN号0378-4371
DOI10.1016/j.physa.2017.11.053
英文摘要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.
资助项目National Natural Science Foundation of China[91646114] ; National Natural Science Foundation of China[61370150] ; Chongqing Research Program of Basic Research and Frontier Technology[cstc2015jcyjB0244] ; Sichuan Provincial Science and Technology Department[2015GZ0143]
WOS研究方向Physics
语种英语
WOS记录号WOS:000423495100104
出版者ELSEVIER SCIENCE BV
源URL[http://119.78.100.138/handle/2HOD01W0/4534]  
专题大数据挖掘及应用中心
通讯作者Shang, Ming-Sheng
作者单位1.Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu 610054, Sichuan, Peoples R China
2.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Qing-Xian,Li, Jian,Luo, Xin,et al. Effects of the bipartite structure of a network on performance of recommenders[J]. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS,2018,492:1257-1266.
APA Wang, Qing-Xian,Li, Jian,Luo, Xin,Xu, Jian-Jun,&Shang, Ming-Sheng.(2018).Effects of the bipartite structure of a network on performance of recommenders.PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS,492,1257-1266.
MLA Wang, Qing-Xian,et al."Effects of the bipartite structure of a network on performance of recommenders".PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 492(2018):1257-1266.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。