Understanding Scaling Laws for Recommendation Models
This study addresses scaling efficiency for recommendation systems, providing insights for resource planning and system development, though it is incremental as it applies known scaling law concepts to a specific domain.
The paper investigates scaling laws for DLRM-style recommendation models, finding that model quality scales with power law plus constant in model size, data size, and compute, and shows that parameter scaling is ineffective while data scaling is the best path forward.
Scale has been a major driving force in improving machine learning performance, and understanding scaling laws is essential for strategic planning for a sustainable model quality performance growth, long-term resource planning and developing efficient system infrastructures to support large-scale models. In this paper, we study empirical scaling laws for DLRM style recommendation models, in particular Click-Through Rate (CTR). We observe that model quality scales with power law plus constant in model size, data size and amount of compute used for training. We characterize scaling efficiency along three different resource dimensions, namely data, parameters and compute by comparing the different scaling schemes along these axes. We show that parameter scaling is out of steam for the model architecture under study, and until a higher-performing model architecture emerges, data scaling is the path forward. The key research questions addressed by this study include: Does a recommendation model scale sustainably as predicted by the scaling laws? Or are we far off from the scaling law predictions? What are the limits of scaling? What are the implications of the scaling laws on long-term hardware/system development?