Transfer collaborative filtering from multiple sources via consensus regularization

被引:9
作者
Zhuang, Fuzhen [1 ,2 ]
Zheng, Jing [3 ]
Chen, Jingwu [1 ,2 ]
Zhang, Xiangliang [4 ]
Shi, Chuan [3 ]
He, Qing [1 ,2 ]
机构
[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
基金
国家重点研发计划; 中国国家自然科学基金;
关键词
Collaborative filtering; Transfer learning; Multiple sources; Consensus regularization;
D O I
10.1016/j.neunet.2018.08.022
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
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.
引用
收藏
页码:287 / 295
页数:9
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