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Trust-embedded collaborative deep generative model for social recommendation

文献类型：期刊论文

作者Deng, Xiaoyi1; Wu, Yenchun Jim2; Zhuang, Fuzhen3
刊名JOURNAL OF SUPERCOMPUTING
出版日期2020-01-30
页码29
关键词Recommender system Deep generative model Collaborative topic regression Trust matrix factorization Deep learning
ISSN号0920-8542
DOI10.1007/s11227-020-03178-1
英文摘要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.
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
语种英语
WOS记录号WOS:000510280400002
出版者SPRINGER
源URL[http://119.78.100.204/handle/2XEOYT63/14770]  
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.

入库方式： OAI收割

来源：计算技术研究所

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