2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.HC2024★ 2 cited
A LLM-based Controllable, Scalable, Human-Involved User Simulator Framework for Conversational Recommender Systems
Lixi Zhu, Xiaowen Huang, Jitao Sang
Conversational Recommender System (CRS) leverages real-time feedback from users to dynamically model their preferences, thereby enhancing the system's ability to provide personaliz…
cs.AI2024
How Reliable is Your Simulator? Analysis on the Limitations of Current LLM-based User Simulators for Conversational Recommendation
Lixi Zhu, Xiaowen Huang, Jitao Sang
Conversational Recommender System (CRS) interacts with users through natural language to understand their preferences and provide personalized recommendations in real-time. CRS has…
cs.IR2021★ 1 cited
Knowledge Graph-enhanced Sampling for Conversational Recommender System
Mengyuan Zhao, Xiaowen Huang, Lixi Zhu +2
The traditional recommendation systems mainly use offline user data to train offline models, and then recommend items for online users, thus suffering from the unreliable estimatio…