Request-Level Training Paradigms for Efficient Large-Scale Recommendation Systems

Abstract

Modern recommendation systems operate at a massive scale and are expected to provide increasingly personalized experiences under strict latency, storage, and computational constraints. Traditional training pipelines typically structure data at the level of individual impressions, which has historically been effective but introduces substantial redundancy in both data representation and computation. This redundancy becomes a significant bottleneck as platforms grow in complexity and volume. Request-level training paradigms offer an alternative by redefining the basic unit of learning from the individual impression to the full request or grouped interaction context. This article examines the conceptual foundations, architectural implications, and systems benefits of request-level training in large-scale recommendation systems. It argues that aligning training data structures more closely with real interaction patterns enables better computational efficiency, richer contextual modeling, and more scalable personalization. The discussion also explores the practical infrastructure changes required for adopting this paradigm and considers its broader implications for the future of machine learning systems used in retrieval and ranking.