The RecSys Challenge 2024 aims to advance news recommendation by addressing both the technical and normative challenges inherent in designing effective and responsible recommender systems for news publishing. This paper describes the challenge, including its objectives, problem setting, and the dataset provided by the Danish news publishers Ekstra Bladet and JP/Politikens Media Group ("Ekstra Bladet"). The challenge explores the unique aspects of news recommendation, such as modeling user preferences based on behavior, accounting for the influence of the news agenda on user interests, and managing the rapid decay of news items. Additionally, the challenge embraces normative complexities, investigating the effects of recommender systems on news flow and their alignment with editorial values. We summarize the challenge setup, dataset characteristics, and evaluation metrics. Finally, we announce the winners and highlight their contributions. The dataset is available at: https://recsys.eb.dk.

本研究探讨了在新闻推荐中同时解决技术与规范性挑战的问题，旨在设计有效且负责任的推荐系统。论文提出了一种新的挑战，特别关注用户偏好的建模及其与新闻议程的关系，同时研究推荐系统对新闻流动的影响与编辑价值的对应关系。重要发现表明，该挑战为优化新闻推荐提供了新视角，有助于提升用户体验和新闻质量。

RecSys挑战2024：在新闻推荐中平衡准确性和编辑价值