Transfer collaborative filtering from multiple sources via consensus regularization

doi:10.1016/j.neunet.2018.08.022

	Transfer collaborative filtering from multiple sources via consensus regularization
	Zhuang, Fuzhen 1,2; Zheng, Jing 3; Chen, Jingwu 1,2; Zhang, Xiangliang 4; Shi, Chuan 3; He, Qing 1,2
	2018-12-01
发表期刊	NEURAL NETWORKS
ISSN	0893-6080
卷号	108 页码:287-295
摘要	Collaborative filtering is one of the most successful approaches to build recommendation systems. Recently, transfer learning has been applied to recommendation systems for incorporating information from external sources. However, most existing transfer collaborative filtering algorithms tend to transfer knowledge from one single source domain. Rich information is available in many source domains, which can better complement the data in the target domain than that from a single source. However, it is common to get inconsistent information from different sources. To this end, we proposed a TRAnsfer collaborative filtering framework from multiple sources via ConsEnsus Regularization, called TRACER for short. The TRACER framework handles the information inconsistency with a consensus regularization, which enforces the outputs from multiple sources to converge. In addition, our algorithm is to learn and transfer knowledge at the same time while most of the traditional transfer learning algorithms are to learn knowledge first and then transfer it. Experiments conducted on two real-world data sets validate the effectiveness of the proposed algorithm. (C) 2018 Elsevier Ltd. All rights reserved.
关键词	Collaborative filtering Transfer learning Multiple sources Consensus regularization
DOI	10.1016/j.neunet.2018.08.022
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China[2018YFB1004300] ; National Natural Science Foundation of China[61773361] ; National Natural Science Foundation of China[61473273] ; National Natural Science Foundation of China[91546122] ; Science and Technology Project of Guangdong Province[2015B010109005] ; Project of Youth Innovation Promotion Association CAS[2017146]
WOS研究方向	Computer Science ; Neurosciences & Neurology
WOS类目	Computer Science, Artificial Intelligence ; Neurosciences
WOS记录号	WOS:000450298900021
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计	被引频次：8[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/4344
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhuang, Fuzhen
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Beijing Univ Posts & Telecommun, Beijing, Peoples R China 4.King Abdullah Univ Sci & Technol, Thuwal, Saudi Arabia
推荐引用方式 GB/T 7714	Zhuang, Fuzhen,Zheng, Jing,Chen, Jingwu,et al. Transfer collaborative filtering from multiple sources via consensus regularization[J]. NEURAL NETWORKS,2018,108:287-295.
APA	Zhuang, Fuzhen,Zheng, Jing,Chen, Jingwu,Zhang, Xiangliang,Shi, Chuan,&He, Qing.(2018).Transfer collaborative filtering from multiple sources via consensus regularization.NEURAL NETWORKS,108,287-295.
MLA	Zhuang, Fuzhen,et al."Transfer collaborative filtering from multiple sources via consensus regularization".NEURAL NETWORKS 108(2018):287-295.