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

文献类型：期刊论文

作者Han, Jiayu1,2; Zheng, Lei3; Xu, Yuanbo1,2; Zhang, Bangzuo4; Zhuang, Fuzhen5,6; Yu, Philip S.3; Zuo, Wanli1,2
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2020-03-01
卷号31期号:3页码:737-748
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
ISSN号2162-237X
DOI10.1109/TNNLS.2019.2909432
英文摘要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.
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
语种英语
WOS记录号WOS:000521961300003
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/14101]  
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.

入库方式： OAI收割

来源：计算技术研究所

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