Hyeri Lee  Heejin Kook  Seongmin Park  Jongwuk Lee

10.5626/JOK.2025.52.10.890

conversational recommender system Large Language Model Recommender System Information Retrieval

Conversational recommendation systems seek to understand user preferences through interaction, providing personalized item suggestions. Recent advancements in large language models (LLMs) have improved the ability to infer latent preferences from conversations; however, including superfluous information can result in unintended recommendations. This study introduces RNCRS (Reasoning paths and Neighbor enhanced CRS), a conversational recommendation framework that develops reasoning paths for LLMs and captures latent preference information beyond surface-level context. The proposed method enables robust recommendations without requiring additional training by (1) utilizing item reasoning paths to represent multifaceted item characteristics, (2) leveraging collaborative knowledge to reflect the collective preference patterns of neighboring users, and (3) complementing explicit preferences that may be overlooked in reasoning paths through direct content similarity between conversations and items. Experimental results demonstrate that the proposed method achieves up to 12.7% improvement in performance over existing models based on Recall@50.