Beyond Retrieval: Generating Narratives in Conversational Recommender Systems

Large Language Models (LLMs) have shown remarkable progress in generating human-quality text and engaging in complex reasoning. This presents a unique opportunity to revolutionize conversational recommender systems by enabling them to generate rich, engaging and personalized narratives that go beyond recommendations. However, the lack of suitable datasets limits research in this area. This paper addresses this challenge by making two key contributions.