  Facilitated by deep neural networks, video recommendation systems have made
significant advances. Existing video recommendation systems directly exploit
features from different modalities (e.g., user personal data, user behavior
data, video titles, video tags, and visual contents) to input deep neural
networks, while expecting the networks to online mine user-preferred topics
implicitly from these features. However, the features lacking semantic topic
information limits accurate recommendation generation. In addition, feature
crosses using visual content features generate high dimensionality features
that heavily downgrade the online computational efficiency of networks. In this
paper, we explicitly separate topic generation from recommendation generation,
propose a multimodal topic learning algorithm to exploit three modalities
(i.e., tags, titles, and cover images) for generating video topics offline. The
topics generated by the proposed algorithm serve as semantic topic features to
facilitate preference scope determination and recommendation generation.
Furthermore, we use the semantic topic features instead of visual content
features to effectively reduce online computational cost. Our proposed
algorithm has been deployed in the Kuaibao information streaming platform.
Online and offline evaluation results show that our proposed algorithm performs
favorably.
