超越单一标签：通过LLM驱动的数据增强改进对话式推荐
Beyond Single Labels: Improving Conversational Recommendation through LLM-Powered Data Augmentation
AI总结：
针对对话式推荐中的假阴性问题，提出基于LLM语义检索与相关性过滤的数据增强框架及两阶段训练策略，在基准数据集上显著提升推荐性能。
摘要：
对话式推荐系统（CRS）通过多轮对话与用户互动，捕捉自然语言交互中的细微偏好，从而提升推荐质量。然而，这些系统常常面临假阴性问题，即用户可能喜欢的项目在训练期间被错误标记为负样本，导致推荐效果欠佳。通过数据增强扩展标签集是一种直观的解决方案，但面临平衡两个关键方面的挑战：确保语义相关性并保留CRS数据集中固有的协同信息。为解决这些问题，我们提出了一种新颖的数据增强框架，该框架首先利用基于LLM的语义检索器来识别多样且语义相关的项目，然后通过相关性评分器进行过滤以去除噪声候选。在此基础上，我们引入了一种两阶段训练策略，以平衡语义相关性和协同信息。在两个基准数据集和用户模拟器上的大量实验表明，各种推荐器均取得了显著且一致的性能提升，凸显了我们的方法在推进CRS性能方面的有效性。
英文摘要：
Conversational recommender systems (CRSs) enhance recommendation quality by engaging users in multi-turn dialogues, capturing nuanced preferences through natural language interactions. However, these systems often face the false negative issue, where items that a user might like are incorrectly labeled as negative during training, leading to suboptimal recommendations.Expanding the label set through data augmentation presents an intuitive solution but faces the challenge of balancing two key aspects: ensuring semantic relevance and preserving the collaborative information inherent in CRS datasets. To address these issues, we propose a novel data augmentation framework that first leverages an LLM-based semantic retriever to identify diverse and semantically relevant items, which are then filtered by a relevance scorer to remove noisy candidates. Building on this, we introduce a two-stage training strategy balancing semantic relevance and collaborative information. Extensive experiments on two benchmark datasets and user simulators demonstrate significant and consistent performance improvements across various recommenders, highlighting the effectiveness of our approach in advancing CRS performance.
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。
