USB-Rec: An Effective Framework for Improving Conversational Recommendation Capability of Large Language Model

• URL: http://arxiv.org/abs/2509.20381v1
• Date: Sat, 20 Sep 2025 22:34:55 GMT
• Title: USB-Rec: An Effective Framework for Improving Conversational Recommendation Capability of Large Language Model
• Authors: Jianyu Wen, Jingyun Wang, Cilin Yan, Jiayin Cai, Xiaolong Jiang, Ying Zhang,
• Abstract summary: Large Language Models (LLMs) have been widely employed in Conversational Recommender Systems (CRSs)<n>In this work, we propose an integrated training-inference framework, User-Simulator-Based framework (USB-Rec)<n>Our method consistently outperforms previous state-of-the-art methods.
• Score: 14.628459519236658
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, Large Language Models (LLMs) have been widely employed in Conversational Recommender Systems (CRSs). Unlike traditional language model approaches that focus on training, all existing LLMs-based approaches are mainly centered around how to leverage the summarization and analysis capabilities of LLMs while ignoring the issue of training. Therefore, in this work, we propose an integrated training-inference framework, User-Simulator-Based framework (USB-Rec), for improving the performance of LLMs in conversational recommendation at the model level. Firstly, we design a LLM-based Preference Optimization (PO) dataset construction strategy for RL training, which helps the LLMs understand the strategies and methods in conversational recommendation. Secondly, we propose a Self-Enhancement Strategy (SES) at the inference stage to further exploit the conversational recommendation potential obtained from RL training. Extensive experiments on various datasets demonstrate that our method consistently outperforms previous state-of-the-art methods.

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