EB-NeRD: A Large-Scale Dataset for News Recommendation

Johannes Kruse, Kasper Lindskow, Saikishore Kalloori, Marco Polignano, Claudio Pomo +4 more
10/4/2024
cs.IRcs.AIcs.LG

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Abstract

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.

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Cite this paper

@article{kruse2024ebnerd,
  title  = {EB-NeRD: A Large-Scale Dataset for News Recommendation},
  author = {Johannes Kruse and Kasper Lindskow and Saikishore Kalloori and Marco Polignano and Claudio Pomo and Abhishek Srivastava and Anshuk Uppal and Michael Riis Andersen and Jes Frellsen},
  year   = {2024},
  eprint = {2410.03432},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2410.03432v1}
}

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