USB-Rec：提升大语言模型对话推荐能力的有效框架
USB-Rec: An Effective Framework for Improving Conversational Recommendation Capability of Large Language Model
AI总结：
本文提出USB-Rec框架，通过偏好优化数据集构建和推理阶段自我增强策略，在模型层面提升大语言模型的对话推荐能力，实验验证其优于现有方法。
摘要：
近年来，大语言模型（LLMs）已被广泛应用于对话推荐系统（CRSs）中。与专注于训练的传统语言模型方法不同，所有现有的基于LLMs的方法主要围绕如何利用LLMs的总结和分析能力，而忽略了训练问题。因此，在这项工作中，我们提出了一个集成的训练-推理框架，即基于用户模拟器的框架（USB-Rec），以在模型层面提升LLMs在对话推荐中的性能。首先，我们设计了一种基于LLM的偏好优化（PO）数据集构建策略，用于强化学习训练，这有助于LLMs理解对话推荐中的策略和方法。其次，我们在推理阶段提出了一种自我增强策略（SES），以进一步挖掘从强化学习训练中获得的对话推荐潜力。在各种数据集上的大量实验表明，我们的方法始终优于之前的最先进方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
