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RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations

Published: 08 October 2024 Publication History

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.

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  • (2024)EB-NeRD a large-scale dataset for news recommendationProceedings of the Recommender Systems Challenge 202410.1145/3687151.3687152(1-11)Online publication date: 14-Oct-2024

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    cover image ACM Conferences
    RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems
    October 2024
    1438 pages
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    Publication History

    Published: 08 October 2024

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    Author Tags

    1. Beyond-Accuracy
    2. Competition
    3. Dataset
    4. Editorial Values
    5. News Recommendations
    6. Recommender Systems

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    • Extended-abstract
    • Research
    • Refereed limited

    Funding Sources

    • Innovation Foundation Denmark
    • PNRR project FAIR - Future AI Research
    • Spoke 6 - Symbiotic AI under the NRRP MUR program funded by the NextGenerationEU
    • Platform Intelligence in News-Project

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    Overall Acceptance Rate 254 of 1,295 submissions, 20%

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    • (2024)EB-NeRD a large-scale dataset for news recommendationProceedings of the Recommender Systems Challenge 202410.1145/3687151.3687152(1-11)Online publication date: 14-Oct-2024

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