Transfer collaborative filtering from multiple sources via consensus regularization
KAUST DepartmentComputer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Online Publication Date2018-09-05
Print Publication Date2018-12
Permanent link to this recordhttp://hdl.handle.net/10754/628811
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AbstractCollaborative 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 TRA nsfer collaborative filtering framework from multiple sources via C onsE nsus R egularization, 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.
CitationZhuang F, Zheng J, Chen J, Zhang X, Shi C, et al. (2018) Transfer collaborative filtering from multiple sources via consensus regularization. Neural Networks 108: 287–295. Available: http://dx.doi.org/10.1016/j.neunet.2018.08.022.
SponsorsThis research work is supported by the National Key R&D Program of China under Grant No. 2018YFB1004300, the National Natural Science Foundation of China under Grant Nos. 61773361, 61473273, 91546122, the Science and Technology Project of Guangdong Province under Grant No.2015B010109005, the Project of Youth Innovation Promotion Association CAS under Grant No. 2017146.