现有新闻推荐数据集限制多样性设计，法律或可打破瓶颈
How public datasets constrain the development of diversity-aware news recommender systems, and what law could do about it
- 识别出构建多样性新闻推荐所需的数据要素
- 发现现有公开数据集在覆盖多样性信息上严重不足
- 提出欧盟法规可作为获取必要数据的制度路径
新闻推荐系统日益决定个体在线所见内容。过去十年，研究者普遍批评以用户参与度为导向的推荐机制。为提供替代方案，学者提出应基于编辑价值（尤其是多样性）推荐新闻，以支持媒体在民主社会中的角色。然而，规范理论与技术实现之间仍存在巨大鸿沟。本文认为，要实现多样性导向的新闻推荐系统，必须关注训练所需的数据集。本文主要贡献有二：其一，识别出支持规范文献中提出的多样性推荐系统所需的数据要素，并评估当前可用公开数据集的局限性及其潜在扩展空间；其二，分析欧洲法律与政策如何为研究者提供结构性数据访问渠道，以推动多样性推荐系统的研发。
原文摘要 · Abstract (English)
News recommender systems increasingly determine what news individuals see online. Over the past decade, researchers have extensively critiqued recommender systems that prioritise news based on user engagement. To offer an alternative, researchers have analysed how recommender systems could support the media's ability to fulfil its role in democratic society by recommending news based on editorial values, particularly diversity. However, there continues to be a large gap between normative theory on how news recommender systems should incorporate diversity, and technical literature that designs such systems. We argue that to realise diversity-aware recommender systems in practice, it is crucial to pay attention to the datasets that are needed to train modern news recommenders. We aim to make two main contributions. First, we identify the information a dataset must include to enable the development of the diversity-aware news recommender systems proposed in normative literature. Based on this analysis, we assess the limitations of currently available public datasets, and show what potential they do have to expand research into diversity-aware recommender systems. Second, we analyse why and how European law and policy can be used to provide researchers with structural access to the data they need to develop diversity-aware news recommender systems.
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