构建百万用户新闻推荐数据集，助力负责任的新闻推荐系统研究
EB-NeRD: A Large-Scale Dataset for News Recommendation
- 基于丹麦媒体埃克斯特拉报数据，涵盖超百万用户与三千万次曝光记录
- 包含12.5万篇新闻文章，含标题、摘要、正文及分类等完整元数据
- 适合作为新闻推荐系统技术与伦理挑战的研究基准，支持公平性评估
个性化内容推荐在视频流媒体到社交媒体的数字媒体体验中至关重要。然而，新闻出版领域存在若干特定挑战，制约了推荐系统的应用。为此，我们推出了《埃克斯特拉报新闻推荐数据集》（EB-NeRD）。该数据集包含超过一百万唯一用户、3700多万条曝光日志，以及超过12.5万篇丹麦语新闻文章，涵盖标题、摘要、正文和类别等元数据。EB-NeRD作为RecSys '24挑战赛的基准数据集，展示了其在解决新闻推荐系统的技术与规范性挑战中的应用潜力。数据集已公开：https://recsys.eb.dk。
原文摘要 · Abstract (English)
Personalized content recommendations have been pivotal to the content experience in digital media from video streaming to social networks. However, several domain specific challenges have held back adoption of recommender systems in news publishing. To address these challenges, we introduce the Ekstra Bladet News Recommendation Dataset (EB-NeRD). The dataset encompasses data from over a million unique users and more than 37 million impression logs from Ekstra Bladet. It also includes a collection of over 125,000 Danish news articles, complete with titles, abstracts, bodies, and metadata, such as categories. EB-NeRD served as the benchmark dataset for the RecSys '24 Challenge, where it was demonstrated how the dataset can be used to address both technical and normative challenges in designing effective and responsible recommender systems for news publishing. The dataset is available at: https://recsys.eb.dk.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。