Modeling Response Uncertainty and Item Relevance in Conversational Recommender Systems

Abstract

Conversational Recommender Systems (CRSs) aim to extract user preferences from natural language interactions and deliver personalized recommendations through conversations. In multi-turn conversations, these systems often generate low-confidence responses when user intent is unclear, leading to inappropriate replies and a degraded user experience. However, existing CRSs rarely assess the confidence of generated responses or detect their semantic uncertainty. Additionally, limited modeling of item-based semantic relevance hinders the system’s ability to produce contextually coherent and preference-aligned recommendations. To this end, we propose a Semantic-enhanced CRS (SCRS) that explicitly models response-based semantic uncertainty and item-based semantic relevance to enhance both response and recommendation quality. Specifically, in conversation tasks, SCRS samples multiple candidate responses and estimates their semantic uncertainty by combining generation probabilities with semantic analysis, serving as a constraint to guide fine-tuning and improve response quality. In recommendation tasks, SCRS enriches item representations by integrating textual knowledge and calculates semantic relevance between items, then applies a dynamic optimization strategy to refine the recommendation process, generating items better aligned with user preferences. Extensive experiments on two public datasets demonstrate that SCRS achieves significant improvements in both recommendation accuracy and response quality over state-of-the-art baselines, validating the effectiveness of our proposed method.