  Sparsity of user-to-item rating data becomes one of challenging issues in the
recommender systems, which severely deteriorates the recommendation
performance. Fortunately, context-aware recommender systems can alleviate the
sparsity problem by making use of some auxiliary information, such as the
information of both the users and items. In particular, the visual information
of items, such as the movie poster, can be considered as the supplement for
item description documents, which helps to obtain more item features. In this
paper, we focus on movie recommender system and propose a probabilistic matrix
factorization based recommendation scheme called visual recurrent convolutional
matrix factorization (VRConvMF), which utilizes the textual and multi-level
visual features extracted from the descriptive texts and posters respectively.
We implement the proposed VRConvMF and conduct extensive experiments on three
commonly used real world datasets to validate its effectiveness. The
experimental results illustrate that the proposed VRConvMF outperforms the
existing schemes.
