通过偏差分析与语言模型增强的数据增强改进对话推荐系统
Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data Augmentation
AI总结：
本研究分析了对话推荐系统基准数据集中的选择偏差和流行度偏差，提出了两种基于语言模型的数据增强策略'Once-Aug'和'PopNudge'，在ReDial和TG-ReDial数据集上有效提升了模型性能并缓解了多种偏差。
摘要：
对话推荐系统（Conversational Recommendation System, CRS）是一个快速发展的研究领域，随着语言建模技术的进步而备受关注。然而，由于其相对新颖且现有研究贡献有限，当前对话推荐面临诸多挑战。本研究深入分析了用于开发CRS模型的基准数据集，并解决了多轮交互固有反馈循环中产生的潜在偏差，包括选择偏差和多种流行度偏差变体。受语言模型生成数据及数据增强技术成功应用的启发，我们提出了两种新策略——'Once-Aug'和'PopNudge'，以提升模型性能同时缓解偏差。通过在ReDial和TG-ReDial基准数据集上的广泛实验，我们证明了数据增强方法对CRS技术的持续改进，并为解决多种新定义的偏差提供了额外见解。
英文摘要：
Conversational Recommendation System (CRS) is a rapidly growing research area that has gained significant attention alongside advancements in language modelling techniques. However, the current state of conversational recommendation faces numerous challenges due to its relative novelty and limited existing contributions. In this study, we delve into benchmark datasets for developing CRS models and address potential biases arising from the feedback loop inherent in multi-turn interactions, including selection bias and multiple popularity bias variants. Drawing inspiration from the success of generative data via using language models and data augmentation techniques, we present two novel strategies, 'Once-Aug' and 'PopNudge', to enhance model performance while mitigating biases. Through extensive experiments on ReDial and TG-ReDial benchmark datasets, we show a consistent improvement of CRS techniques with our data augmentation approaches and offer additional insights on addressing multiple newly formulated biases.
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。
