Research in news recommendation systems (NRS) continues to explore the best ways to integrate normative goals such as editorial objectives and public service values into existing systems. Prior efforts have incorporated expert input or audience feedback to quantify these values, laying the groundwork for more civic-minded recommender systems. This paper contributes to that trajectory, introducing a method for embedding civic values into NRS through large-scale, structured audience evaluations. The proposed civic ground truth approach aims to generate value-based labels through a nationally representative survey that are generalisable across a wider news corpus, using automated metadata enrichment.

翻译：新闻推荐系统（NRS）的研究持续探索将规范性目标（如编辑目标和公共服务价值）整合到现有系统中的最佳方法。先前的研究通过纳入专家意见或受众反馈来量化这些价值，为更具公民意识的推荐系统奠定了基础。本文延续这一脉络，提出了一种通过大规模、结构化的受众评估将公民价值嵌入NRS的方法。所提出的公民基础事实方法旨在通过一项具有全国代表性的调查生成基于价值的标签，并利用自动化的元数据增强技术，使这些标签能够推广到更广泛的新闻语料库中。