RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929521933942784 |
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| author | Kruse, Johannes Lindskow, Kasper Kalloori, Saikishore Polignano, Marco Pomo, Claudio Srivastava, Abhishek Uppal, Anshuk Andersen, Michael Riis Frellsen, Jes |
| author_facet | Kruse, Johannes Lindskow, Kasper Kalloori, Saikishore Polignano, Marco Pomo, Claudio Srivastava, Abhishek Uppal, Anshuk Andersen, Michael Riis Frellsen, Jes |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_20483 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations Kruse, Johannes Lindskow, Kasper Kalloori, Saikishore Polignano, Marco Pomo, Claudio Srivastava, Abhishek Uppal, Anshuk Andersen, Michael Riis Frellsen, Jes Information Retrieval Artificial Intelligence Machine Learning 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. |
| title | RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations |
| topic | Information Retrieval Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2409.20483 |