RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations
arXiv:2409.20483 · doi:10.1145/3640457.3687164
Abstract
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.
5 pages, 3 tables, RecSys' 24
References in corpus (1)
Cited by in corpus (5)
- EB-NeRD: A Large-Scale Dataset for News Recommendation
- D-RDW: Diversity-Driven Random Walks for News Recommender Systems
- Informfully Recommenders -- Reproducibility Framework for Diversity-aware Intra-session Recommendations
- Normative Alignment of Recommender Systems via Internal Label Shift
- ZoRRO: A Zero-Weight Personalized Recommender System for Scalable News Recommendation