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

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arxiv 2410.03432 v1 pith:VL2HQSIQ submitted 2024-10-04 cs.IR cs.AIcs.LG

Johannes Kruse , Kasper Lindskow , Saikishore Kalloori , Marco Polignano , Claudio Pomo , Abhishek Srivastava , Anshuk Uppal , Michael Riis Andersen
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Jes Frellsen
This is my paper · ORCID
classification cs.IRcs.AIcs.LG
keywords datasetnewschallengeseb-nerdaddressbladetcontentekstra
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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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Cited by 1 Pith paper

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  1. Revisiting Language Models in Neural News Recommender Systems

    cs.IR 2025-01 conditional novelty 5.0 of 10

    Larger language models as news encoders do not consistently improve recommendation accuracy, but they do improve performance for cold-start users, at higher fine-tuning and compute cost.

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