BERT4SessRec: Content-Based Video Relevance Prediction with Bidirectional Encoder Representations from Transformer

This paper describes our solution for the Content-Based Video Relevance Prediction (CBVRP) challenge, where the task is to predict user click-through behavior on new TV series or new movies according to the user's historical behavior. We consider the task as a session-based recommendation problem and we focus on the modeling of the session. Thus, we use the Bidirectional Encoder Representations from Transformer (BERT) methodology and propose a BERT for session-based recommendation (BERT4SessRec) method. Our method has two stages: in the pre-training stage, we use all sessions as training data and train the bidirectional session encoder with the masking trick; in the fine-tuning stage, we use the provided click-through data and train the click-through prediction network. Our method achieves session representations with the help of BERT, which effectively captures the bidirectional correlation in each session. In addition, the pre-training stage makes full use of all sessions, overcoming the positive-negative imbalance problem of the click-through data. We report the results of using different kinds of features on the test set of the challenge, which verify the effectiveness of our method.