Content-based video relevance prediction with multi-view multi-level deep interest network

This paper presents our solution for the Hulu Content-Based Video Relevance Prediction (CBVRP) challenge, which focuses on cold-start videos as candidates. The keys to success of this prediction scenario are to learn effective user and video representations. To this end, we develop a multi-view multi-level deep interest network (MMDIN), which involves a multi-level deep interest network to learn user and video representations in a single-view, and a late fusion technique to integrate their multi-view representations corresponding to different types of video features. Through the above manner, the cold-start video prediction could be handled well with representations through their past interaction behaviors with videos and video representations based on their multiple types of content profiles.