Conversational Recommender System

Conversational recommender systems (CRSs) aim to improve the user experience of recommendation systems by engaging users in natural language dialogues to understand their preferences and provide personalized suggestions. Current research emphasizes enhancing the credibility and explainability of recommendations, leveraging large language models (LLMs) for improved dialogue management and intent understanding, and incorporating external knowledge bases to enrich recommendations. This active research area is significant because it addresses limitations of traditional recommender systems by creating more engaging and trustworthy interactions, ultimately leading to more effective and satisfying user experiences.