Enhancing Online Ranking Systems via Multi-SurfaceCo-Training for Content Understanding
Abstract
Content understanding is an important part in real-world recommendation systems. This paper introduces a Multi-surface Co-training (MulCo) system, designed to enhance online ranking systems by improving content understanding. The model is trained through a task-aligned co-training approach, leveraging objectives and data from multiple surfaces and various pre-trained em-beddings. It separates video content understanding into an offline model, enabling scalability and efficient resource use. Experiments demonstrate that MulCo significantly outperforms non-task-aligned pre-trained embeddings and achieves substantial gains in online satisfied engagement metrics. This system presents a practical solution to improve content understanding in multi-surface large-scale recommender systems.
