Cold-start representation learning: A recommendation approach with BeRt4Movie and Movie2vec

Abstract

Video relevance computation is one of the most important tasks for the personalized online streaming service. Given the relevance of videos and viewer feedbacks, the system can provide personalized recommendations, which helps viewers discover more contents of interest in most online services. However, the computation of a video relevance table is based on viewers' implicit feedbacks such as watch and search history, which perform poorly for newly added “cold-start” videos. Facing the cold start problem, we introduce a recommendation method with Bidirectional Encoder Representations from Transformers, which considers the continuity of ordered watching plan and trained the sequence of path from start to end named Bert4Movie. What's more, we propose a method named Movie2Vec to represent the videos in a different way. Our method has been used in our solutions of Content-based Video Relevance Prediction Challenge and got a significant improvement in the AUC.