Transformers in music recommendation

In this post we discuss how we’ve applied transformers, which are well-suited to processing sequences of input data, to improve the recommendation system in YouTube Music. This recommendation system consists of three key stages: item retrieval, item ranking, and filtering. Prior user actions are usually added to the ranking models as an input feature. Our approach adapts the Transformer architecture from generative models for the task of understanding the sequential nature of user actions, and blends that with ranking models personalized for that user. Using transformers to incorporate different user actions based on the current user context helps steer music recommendations directly towards the user’s current need. For signed-in users, this approach allows us to incorporate a user’s history without having to explicitly identify what in a user’s history is valuable to the ranking task.