Exploiting Aesthetic Features in Visual Contents for Movie Recommendation

Xiaojie Chen, Pengpeng Zhao, Yanchi Liu, Lei Zhao, Junhua Fang, Victor S. Sheng, Zhiming Cui

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


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.

Original languageEnglish
Article number8688388
Pages (from-to)49813-49821
Number of pages9
JournalIEEE Access
StatePublished - 2019


  • Movie recommendation
  • aesthetic features
  • probabilistic matrix factorization


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