Sequence learning: A paradigm shift for personalized ads recommendations

What this post added

This post introduces a significant evolution in Meta's AI capabilities by detailing a paradigm shift in ads recommendation systems. It moves from traditional Deep Learning Recommendation Models (DLRMs) that relied on human-engineered features to a new system based on sequence learning. This involves event-based learning and the development of new sequence learning architectures that leverage sequential information from user engagement and conversion events. The post highlights the limitations of DLRMs in capturing sequential and granular information, and outlines the technical advancements, including event-based features (EBFs) and custom transformer architectures, implemented to achieve this shift. It also discusses the scaling challenges and solutions for both sequence learning architectures and the richness of event sequences, ultimately leading to improved ad relevance and performance.