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.
ACM Multimedia