Multi-Agentic Recommender Systems: Foundations, Design Patterns, and E-Commerce Applications — An Industrial Tutorial

The goal of this tutorial is to provide our perspective on the most recent advances in LLM-powered agents for recommender systems. Building on our extensive experience deploying agentic tools in large-scale environments, this tutorial hopes to deepen the understanding of participants with diverse backgrounds on the alphabets that underpin multi-agentic frameworks. Organized by the founders of leading agentic tools, the tutorial will highlight how these frameworks are being applied to create next-generation recommender systems in diverse applications. The examples include context-aware recommendation, dynamic multi-step orchestration, and personalized recommendation systems. To provide a solid foundation, we begin with a brief background on the evolution of recommender systems and how recent breakthroughs in large language models (LLMs) have shifted the paradigm toward more interactive, adaptive, and autonomous systems. The hands-on session will allow participants to directly engage with state-of-the-art techniques, bridging the gap between theoretical concepts and practical implementations.