3 papers
cs.IR2026
Collaborative User Prompt for Personalized Generative Recommendation
Jerome Ramos, Bin Wu, Aldo Lipani
Large Language Models (LLMs) have become powerful foundations for generative recommender systems, framing recommendation tasks as text generation tasks. However, existing generativ…
cs.AI2026
Interplay: Training Independent Simulators for Reference-Free Conversational Recommendation
Jerome Ramos, Feng Xia, Xi Wang +4
Training conversational recommender systems (CRS) requires extensive dialogue data, which is challenging to collect at scale. To address this, researchers have used simulated user-…
cs.CL2025
PREF: Reference-Free Evaluation of Personalised Text Generation in LLMs
Xiao Fu, Hossein A. Rahmani, Bin Wu +3
Personalised text generation is essential for user-centric information systems, yet most evaluation methods overlook the individuality of users. We introduce \textbf{PREF}, a \text…