Adaptive Deep Modeling of Users and Items Using Side Information for Recommendation

doi:10.1109/TNNLS.2019.2909432

	Adaptive Deep Modeling of Users and Items Using Side Information for Recommendation
	Han, Jiayu 1,2; Zheng, Lei 3; Xu, Yuanbo 1,2; Zhang, Bangzuo 4; Zhuang, Fuzhen 5,6; Yu, Philip S.3; Zuo, Wanli 1,2
	2020-03-01
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
卷号	31 期号:3 页码:737-748
摘要	In the existing recommender systems, matrix factorization (MF) is widely applied to model user preferences and item features by mapping the user-item ratings into a low-dimension latent vector space. However, MF has ignored the individual diversity where the user's preference for different unrated items is usually different. A fixed representation of user preference factor extracted by MF cannot model the individual diversity well, which leads to a repeated and inaccurate recommendation. To this end, we propose a novel latent factor model called adaptive deep latent factor model (ADLFM), which learns the preference factor of users adaptively in accordance with the specific items under consideration. We propose a novel user representation method that is derived from their rated item descriptions instead of original user-item ratings. Based on this, we further propose a deep neural networks framework with an attention factor to learn the adaptive representations of users. Extensive experiments on Amazon data sets demonstrate that ADLFM outperforms the state-of-the-art baselines greatly. Also, further experiments show that the attention factor indeed makes a great contribution to our method.
关键词	Adaptation models Recommender systems Feature extraction Computational modeling Predictive models Task analysis Adaptive systems Adaptive user preference model attention factor convolutional neural network (CNN) recommendation system
DOI	10.1109/TNNLS.2019.2909432
收录类别	SCI
语种	英语
资助项目	Scientific and Technological Development Program of Jilin Province[20180101330JC] ; Scientific and Technological Development Program of Jilin Province[20190302029GX] ; National Natural Science Foundation of China[61602057] ; National Natural Science Foundation of China[61773361]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000521961300003
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：56[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/2XEOYT63/14102
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zuo, Wanli
作者单位	1.Jilin Univ, Dept Comp Sci & Technol, Changchun 130012, Peoples R China 2.Jilin Univ, Minist Educ, Key Lab Symbol Computat & Knowledge Engn, Changchun 130012, Peoples R China 3.Univ Illinois, Dept Comp Sci, Chicago, IL 60661 USA 4.Northeast Normal Univ, Sch Informat Sci & Technol, Changchun 130117, Peoples R China 5.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 6.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Han, Jiayu,Zheng, Lei,Xu, Yuanbo,et al. Adaptive Deep Modeling of Users and Items Using Side Information for Recommendation[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2020,31(3):737-748.
APA	Han, Jiayu.,Zheng, Lei.,Xu, Yuanbo.,Zhang, Bangzuo.,Zhuang, Fuzhen.,...&Zuo, Wanli.(2020).Adaptive Deep Modeling of Users and Items Using Side Information for Recommendation.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,31(3),737-748.
MLA	Han, Jiayu,et al."Adaptive Deep Modeling of Users and Items Using Side Information for Recommendation".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 31.3(2020):737-748.