Exploiting Visual Contents in Posters and Still Frames for Movie Recommendation

IEEE Access

Victor S. Sheng¹  Pengpeng Zhao²  Xiaojie Chen²  Lei Zhao²  Zhixu Li²  Yanchi Liu³  Jiajie Xu⁴  Zhiming Cui⁵ 

[1] Computer Science Department, University of Central Arkansas, Conway, AR, USA;Institute of Artificial Intelligence, Soochow University, Suzhou, China;Management Science and Information Systems, Rutgers University, New Brunswick, NJ, USA;School of Computer Science and Technology, Soochow University, Suzhou, China;School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China;

关键词: Movie recommendation;    visual contents;    probabilistic matrix factorization;   

DOI  :  10.1109/ACCESS.2018.2879971

来源: DOAJ

【 摘 要 】

Recommender systems, e.g., movie recommendation, play an important role in our life. However, few movie recommendation methods have considered the rich visual content information in posters and still frames, which can be used to alleviate the data sparsity and cold start problems in recommendation. Moreover, no existing paper has taken visual feature learning and recommendation into a unified optimization process. To this end, in this paper, we focus on how to use visual contents to improve the performance of movie recommendation and propose a novel movie recommendation model named unified visual contents matrix factorization (UVMF) that integrates visual feature extraction and recommendation into a unified framework. Specifically, we integrate convolutional neural network into probabilistic matrix factorization, and the model can be trained end-to-end. Moreover, we unfix weights in the last few layers of VGG16 to learn features and adapt them for the movie recommendation task. Finally, the experimental results on real-world data show that UVMF outperforms other benchmark methods in terms of recommendation accuracy.

【 授权许可】

Unknown