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A novel hybrid deep recommendation system to differentiate user's preference and item's attractiveness
Zhang, Xiaofeng1; Liu, Huijie1; Chen, Xiaoyun2; Zhong, Jingbin1; Wang, Di3
2020-05
Source PublicationInformation Sciences
ISSN0020-0255
Volume519Pages:306-316
Abstract

With the fast development of online E-commerce Websites and mobile applications, users’ auxiliary information as well as products’ textual information can be easily collected to form a vast amount of training data. Therefore, research efforts are urgently needed to make customized recommendations using such large but sparse data. Deep recommendation model is a natural choice for this research issue. However, most existing approaches try to investigate either user's auxiliary information such as age and zipcode, or item's textual information such as product descriptions, reviews or comments. Therefore, it is desired to see whether user's auxiliary information and item's textual information could be modeled simultaneously. This paper proposes a novel approach which is essentially a hybrid probabilistic matrix factorization model. Particularly, it has two sub components. One component tries to predict user's rating scores by capturing user's personal preferences extracted from auxiliary information. Another component tries to model item's textual attractiveness to different users via a proposed attention based convolutional neural network. We then propose a global objective function and optimize these two sub components under a unified framework. Extensive experiments are performed on five real-world datasets, i.e., ML-100K, ML-1M, ML-10M, AIV and Amazon sub dataset. The promising experimental results have demonstrated the superiority of our proposed approach when compared with both baseline models and state-of-the-art deep recommendation approaches, i.e., PMF, CDL, CTR, ConvMF, ConvMF+ and D-Attn with respect to RMSE criterion.

KeywordProbabilistic Matrix Factorization Deep Learning Recommendation Systems
DOI10.1016/j.ins.2020.01.044
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems
WOS IDWOS:000522097600019
PublisherELSEVIER SCIENCE INC, STE 800, 230 PARK AVE, NEW YORK, NY 10169
Scopus ID2-s2.0-85078856376
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Cited Times [WOS]:19   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorZhang, Xiaofeng
Affiliation1.School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China
2.Faculty of Business Administration, University of Macau, China
3.Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly, Nanyang Technological University, Singapore
Recommended Citation
GB/T 7714
Zhang, Xiaofeng,Liu, Huijie,Chen, Xiaoyun,et al. A novel hybrid deep recommendation system to differentiate user's preference and item's attractiveness[J]. Information Sciences,2020,519:306-316.
APA Zhang, Xiaofeng,Liu, Huijie,Chen, Xiaoyun,Zhong, Jingbin,&Wang, Di.(2020).A novel hybrid deep recommendation system to differentiate user's preference and item's attractiveness.Information Sciences,519,306-316.
MLA Zhang, Xiaofeng,et al."A novel hybrid deep recommendation system to differentiate user's preference and item's attractiveness".Information Sciences 519(2020):306-316.
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