Conversational Recommendation

Conversational recommendation systems (CRSs) aim to improve recommendation accuracy and user satisfaction by engaging users in natural language dialogues to elicit preferences. Current research focuses on enhancing CRSs through techniques like knowledge graph integration to better understand item relationships, leveraging large language models (LLMs) for dialogue management and response generation, and developing more realistic user simulators for evaluation. These advancements are significant because they address limitations of traditional recommender systems and pave the way for more personalized and engaging user experiences across various applications, including e-commerce and mental health support.