Xunyi Jiang Southern University of Science and Technology xunyijiang001@gmail.com

This report explores the evolving landscape of Conversational Recommender Systems (CRS) in the context of Large Language Models (LLMs). It delves into the integration of knowledge graphs and advanced computational techniques to enhance the capabilities of CRS. The report categorizes current CRS methods into four main classes: knowledge-based, goal-driven, fine-tuning, and agent-based approaches, providing an in-depth analysis of each. Special attention is given to how these systems can optimize conversations and recommendations by harnessing the power of LLMs and external knowledge sources. The innovative use of agent-based methods and the challenges of fine-tuning LLMs for CRS are also discussed, highlighting the importance of efficient retrieval methods and the integration of external knowledge for improved system performance.