Trust-embedded collaborative deep generative model for social recommendation

doi:10.1007/s11227-020-03178-1

	Trust-embedded collaborative deep generative model for social recommendation
	Deng, Xiaoyi 1; Wu, Yenchun Jim 2; Zhuang, Fuzhen 3
	2020-01-30
发表期刊	JOURNAL OF SUPERCOMPUTING
ISSN	0920-8542
页码	29
摘要	Social networks can provide massive amounts of information for communication among users and communities. The trust relationships in social networks can be utilized to reveal user preferences for improving the quality of social recommendation, which aims to mitigate information overload and provide users with the most attractive and relevant items or services. However, the data sparsity and cold-start issue degrade recommendation performance significantly. To address these issues, a novel trust-embedded collaborative deep generative model (TCDG) is proposed for exploiting multisource information (content, rating and trust) to predict ratings. TCDG employs deep generative model to unsupervisedly learn deep latent representations for item content through an inference network in latent space instead of observation space. Meanwhile, TCDG adopts probabilistic matrix factorization to map users into low-dimensional latent feature spaces by trust relationships, which can reflect users' mutual influence on the formation of users' opinions more accurately and learn better implicit relationships between items and users from content, rating and trust. In addition, an approach with an annealing parameter to calculate the maximum a posteriori estimates is proposed to learn model parameters. Experiments using four real-world datasets are conducted to evaluate the prediction and top-ranking performance of our model. The results indicate that TDCG has better accuracy and robustness than other methods for making recommendations.
关键词	Recommender system Deep generative model Collaborative topic regression Trust matrix factorization Deep learning
DOI	10.1007/s11227-020-03178-1
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[71401058] ; National Natural Science Foundation of China[71672023] ; Program for New Century Excellent Talents in Fujian Province University (NCETFJ)[Z1625110] ; Ministry of Science & Technology, Taiwan[MOST 108-2511-H-003-034-MY2]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000510280400002
出版者	SPRINGER
引用统计	被引频次：6[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/2XEOYT63/14771
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wu, Yenchun Jim
作者单位	1.Huaqiao Univ, Business Sch, Quanzhou 362021, Peoples R China 2.Natl Taiwan Normal Univ, Grad Inst Global Business & Strategy, Taipei 10645, Taiwan 3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Deng, Xiaoyi,Wu, Yenchun Jim,Zhuang, Fuzhen. Trust-embedded collaborative deep generative model for social recommendation[J]. JOURNAL OF SUPERCOMPUTING,2020:29.
APA	Deng, Xiaoyi,Wu, Yenchun Jim,&Zhuang, Fuzhen.(2020).Trust-embedded collaborative deep generative model for social recommendation.JOURNAL OF SUPERCOMPUTING,29.
MLA	Deng, Xiaoyi,et al."Trust-embedded collaborative deep generative model for social recommendation".JOURNAL OF SUPERCOMPUTING (2020):29.