Improving Training Stability for Multitask Ranking Models in Recommender Systems
Abstract
Recommender systems play an important role in YouTube, one of the largest online video platforms across the world. In this paper, we focus on a real-world multitask ranking model for YouTube recommendations.
While most of the recommendation research is dedicated to designing better models to improve user engagement and satisfaction, we found that research on stabilizing the training for such models is severely under-explored.
As the recommendation models become larger and more sophisticated, they are more vulnerable to training instability issues, \emph{i.e.}, the loss diverges (instead of converging) which can make the model unusable, wasting significant resources and blocking model iterations.
In this paper, we share our understanding and best practices we learned for improving the training stability of a multitask ranking model used in production. We show some properties of the model that lead to unstable training and speculate on the cause. Furthermore, we propose an effective solution to improve training stability based on our observations of training dynamics when model training starts to become unstable. Our experiments on a proprietary dataset show the effectiveness of the proposed method over several commonly used baseline methods.
While most of the recommendation research is dedicated to designing better models to improve user engagement and satisfaction, we found that research on stabilizing the training for such models is severely under-explored.
As the recommendation models become larger and more sophisticated, they are more vulnerable to training instability issues, \emph{i.e.}, the loss diverges (instead of converging) which can make the model unusable, wasting significant resources and blocking model iterations.
In this paper, we share our understanding and best practices we learned for improving the training stability of a multitask ranking model used in production. We show some properties of the model that lead to unstable training and speculate on the cause. Furthermore, we propose an effective solution to improve training stability based on our observations of training dynamics when model training starts to become unstable. Our experiments on a proprietary dataset show the effectiveness of the proposed method over several commonly used baseline methods.
Research Areas
