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Exploiting Aesthetic Features in Visual Contents for Movie Recommendation

文献类型：期刊论文

作者Chen, Xiaojie5; Cui, Zhiming1; Sheng, Victor S.2; Fang, Junhua5; Zhao, Lei5; Liu, Yanchi3; Zhao, Pengpeng4,5
刊名IEEE ACCESS
出版日期2019
卷号7页码:49813-49821
关键词Movie recommendation aesthetic features probabilistic matrix factorization
ISSN号2169-3536
DOI10.1109/ACCESS.2019.2910722
英文摘要As one of the most widely used recommender systems, movie recommendation plays an important role in our life. However, the data sparsity problem severely hinders the effectiveness of personalized movie recommendation, which requires more rich content information to be utilized. Posters and still frames, which directly display the visual contents of movies, have significant influences on movie recommendation. They not only reveal rich knowledge for understanding movies but also useful for understanding user preferences. However, existing recommendation methods rarely consider aesthetic features, which tell how the movie looks and feels, extracted from these pictures for the movie recommendation. To this end, in this paper, we propose an aesthetic-aware unified visual content matrix factorization (called UVMF-AES) to integrate visual feature learning and recommendation into a unified framework. Specifically, we first integrate the convolutional neural network (CNN) features and aesthetic features into probabilistic matrix factorization. Then we establish a unified optimization framework with these features for the movie recommendation. The experimental results on two real-world datasets show that our proposed method UVMF-AES is significantly superior to the state-of-the-art methods on movie recommendation.
资助项目NSFC[61876217] ; NSFC[61872258] ; NSFC[61728205] ; Suzhou Science and Technology Development Program[SYG201803] ; Postdoctoral Research Foundation of China[2017M621813] ; Natural Science Fund for Colleges and Universities in Jiangsu Province[18KJB520044] ; Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences[IIP2019-1]
WOS研究方向Computer Science ; Engineering ; Telecommunications
语种英语
WOS记录号WOS:000466916200001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/4257]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhao, Pengpeng
作者单位1.Suzhou Univ Sci & Technol, Sch Elect & Informat Engn, Suzhou 215009, Peoples R China
2.Univ Cent Arkansas, Comp Sci Dept, Conway, AR 72035 USA
3.Rutgers State Univ, Management Sci & Informat Syst, New Brunswick, NJ 08901 USA
4.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
5.Soochow Univ, Sch Comp Sci & Technol, Inst Artificial Intelligence, Suzhou 215006, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Xiaojie,Cui, Zhiming,Sheng, Victor S.,et al. Exploiting Aesthetic Features in Visual Contents for Movie Recommendation[J]. IEEE ACCESS,2019,7:49813-49821.
APA Chen, Xiaojie.,Cui, Zhiming.,Sheng, Victor S..,Fang, Junhua.,Zhao, Lei.,...&Zhao, Pengpeng.(2019).Exploiting Aesthetic Features in Visual Contents for Movie Recommendation.IEEE ACCESS,7,49813-49821.
MLA Chen, Xiaojie,et al."Exploiting Aesthetic Features in Visual Contents for Movie Recommendation".IEEE ACCESS 7(2019):49813-49821.

入库方式： OAI收割

来源：计算技术研究所

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