SERA: Learning When and What to Augment for Conversational Recommendation

Conversational recommender systems (CRSs) infer user preferences from multi-turn dialogues to rank items and generate responses. However, the amount and quality of preference evidence vary across conversations: some dialogues already provide sufficient information for reliable recommendation, while others require additional evidence. Existing approaches mainly improve CRS by incorporating auxiliary information, but often assume augmentation is universally beneficial without considering whether augmentation is necessary or what information can effectively compensate for the missing preference evidence. We propose SERA, a sufficiency-aware evidence retrieval and augmentation framework that learns when and what to augment for conversational recommendation. SERA formulates auxiliary information utilization as a conditional evidence augmentation process. It first assesses the sufficiency of current conversational evidence and activates augmentation only when additional information is needed. Then, it retrieves historically relevant evidence that is compatible with the user’s structured preferences and integrates the selected information into both recommendation ranking and response generation. Experiments on ReDial and INSPIRED demonstrate the effectiveness of SERA, achieving relative improvements of 3.26%/1.95% in Recall@10/Recall@50 on ReDial and 5.53%/4.78% on INSPIRED over the best existing methods, while consistently improving automatic response-generation metrics.