  The rapid growth of edge data generated by mobile devices and applications
deployed at the edge of the network has exacerbated the problem of information
overload. As an effective way to alleviate information overload, recommender
system can improve the quality of various services by adding application data
generated by users on edge devices, such as visual and textual information, on
the basis of sparse rating data. The visual information in the movie trailer is
a significant part of the movie recommender system. However, due to the
complexity of visual information extraction, data sparsity cannot be remarkably
alleviated by merely using the rough visual features to improve the rating
prediction accuracy. Fortunately, the convolutional neural network can be used
to extract the visual features precisely. Therefore, the end-to-end neural
image caption (NIC) model can be utilized to obtain the textual information
describing the visual features of movie trailers. This paper proposes a trailer
inception probabilistic matrix factorization model called Ti-PMF, which
combines NIC, recurrent convolutional neural network, and probabilistic matrix
factorization models as the rating prediction model. We implement the proposed
Ti-PMF model with extensive experiments on three real-world datasets to
validate its effectiveness. The experimental results illustrate that the
proposed Ti-PMF outperforms the existing ones.
