TY - GEN
T1 - EB-NeRD a large-scale dataset for news recommendation
AU - Kruse, Johannes
AU - Lindskow, Kasper
AU - Kalloori, Saikishore
AU - Polignano, Marco
AU - Pomo, Claudio
AU - Srivastava, Abhishek
AU - Uppal, Anshuk
AU - Andersen, Michael Riis
AU - Frellsen, Jes
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Recommender Systems
KW - News Recommendations
KW - Dataset
KW - Beyond- Accuracy
KW - Editorial Values
U2 - 10.1145/3687151.3687152
DO - 10.1145/3687151.3687152
M3 - Article in proceedings
T3 - Proceedings of the Recommender Systems Challenge 2024
BT - Proceedings of the RecSys Challenge ’24
PB - Association for Computing Machinery
T2 - RecSys Challenge 2024
Y2 - 14 October 2024 through 18 October 2024
ER -