  Following the popularisation of media streaming, a number of video streaming
services are continuously buying new video content to mine the potential profit
from them. As such, the newly added content has to be handled well to be
recommended to suitable users. In this paper, we address the new item
cold-start problem by exploring the potential of various deep learning features
to provide video recommendations. The deep learning features investigated
include features that capture the visual-appearance, audio and motion
information from video content. We also explore different fusion methods to
evaluate how well these feature modalities can be combined to fully exploit the
complementary information captured by them. Experiments on a real-world video
dataset for movie recommendations show that deep learning features outperform
hand-crafted features. In particular, recommendations generated with deep
learning audio features and action-centric deep learning features are superior
to MFCC and state-of-the-art iDT features. In addition, the combination of
various deep learning features with hand-crafted features and textual metadata
yields significant improvement in recommendations compared to combining only
the former.
