Jure Leskovec introduces Relational Foundation Models (RFMs), a new class of models based on graph neural networks that learn directly from raw, multi-table enterprise data. This approach bypasses manual feature engineering, leading to more accurate, faster-to-deploy, and easier-to-maintain predictive models for tasks like churn prediction, fraud detection, and recommendation systems.
This summary explores Arpita Vats's insights on how Large Language Models (LLMs) are revolutionizing recommender systems. It contrasts the traditional feature-engineering-heavy approach with the contextual understanding of LLMs, which shifts the focus to prompt engineering. Key challenges like inference latency and cost are discussed, along with practical solutions such as lightweight models, knowledge distillation, and hybrid architectures. The conversation also touches on advanced applications like sequential recommendation and the future potential of agentic AI.