Manato Tajiri

Refining Text Generation for Realistic Conversational Recommendation via Direct Preference Optimization

Conversational Recommender Systems (CRSs) aim to elicit user preferences via natural dialogue to provide suitable item recommendations. However, current CRSs often deviate from realistic human interactions by rapidly recommending items in brief sessions. This work addresses this gap by leveraging La