Navigating Large Language Models for Recommendation: From Architecture to Learning Paradigms and Deployment

Large Language Models (LLMs) are reshaping the landscape of recommender systems, giving rise to the emerging field of LLM4Rec that attracts both academia and industry. Unlike earlier approaches that simply borrowed model architectures or learning paradigms from language models, recent advances have led to a dedicated and evolving technical stack for LLM4Rec, spanning architecture design, pre-training and post-training strategies, inference techniques, and real-world deployment. This tutorial offers a systematic and in-depth overview of LLM4Rec through the lens of this technical stack. We will examine how LLMs are being adapted to recommendation tasks across different stages, empowering them with capabilities such reasoning, planning, and in-context learning. Moreover, we will highlight practical challenges including complex user modeling, trustworthiness, and evaluation. Distilling insights from recent research and identifying open problems, this tutorial aims to equip participants with a comprehensive understanding of LLM4Rec and inspire continued innovation in this rapidly evolving field.