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

Johannes Kruse; Kasper Lindskow; Saikishore Kalloori; Marco Polignano; Claudio Pomo; Abhishek Srivastava; Anshuk Uppal; Michael Riis Andersen; Jes Frellsen

doi:10.1145/3640457.3687164

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

Johannes Kruse
, Kasper Lindskow
, Saikishore Kalloori
, Marco Polignano
, Claudio Pomo
, Abhishek Srivastava
, Anshuk Uppal
, Michael Riis Andersen
, Jes Frellsen

Department of Management, Society and Communication

JP/Politikens Hus
Technical University of Denmark
Swiss Federal Institute of Technology in Zurich
University of Bari Aldo Moro
Polytechnic University of Bari
Indian Institute of Management Visakhapatnam

Research output: Chapter in Book/Report/Conference proceeding › Conference abstract in proceedings › Research › peer-review

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.

Original language	English
Title of host publication	RecSys 2024 - Proceedings of the 18th ACM Conference on Recommender Systems
Editors	Tommaso Di Noia, Pasquale Lops, Thorsten Joachims, Katrien Verbert, Pablo Castells, Zhenhua Dong, Ben London
Number of pages	5
Place of Publication	New York
Publisher	Association for Computing Machinery
Publication date	2024
Pages	1195-1199
ISBN (Electronic)	9798400705052
DOIs	https://doi.org/10.1145/3640457.3687164
Publication status	Published - 2024
Event	18th ACM Conference on Recommender Systems. Rec Sys 2024 - Bari, Italy Duration: 14 Oct 2024 → 18 Oct 2024 Conference number: 18 https://recsys.acm.org/recsys24/

Conference

Conference	18th ACM Conference on Recommender Systems. Rec Sys 2024
Number	18
Country/Territory	Italy
City	Bari
Period	14/10/2024 → 18/10/2024
Internet address	https://recsys.acm.org/recsys24/

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Kruse , J., Lindskow, K., Kalloori, S., Polignano, M., Pomo, C., Srivastava, A., Uppal, A., Riis Andersen, M., & Frellsen, J. (2024). RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations. In T. Di Noia, P. Lops, T. Joachims, K. Verbert, P. Castells, Z. Dong, & B. London (Eds.), RecSys 2024 - Proceedings of the 18th ACM Conference on Recommender Systems (pp. 1195-1199). Association for Computing Machinery. https://doi.org/10.1145/3640457.3687164

Kruse , Johannes ; Lindskow, Kasper ; Kalloori, Saikishore et al. / RecSys Challenge 2024 : Balancing Accuracy and Editorial Values in News Recommendations. RecSys 2024 - Proceedings of the 18th ACM Conference on Recommender Systems. editor / Tommaso Di Noia ; Pasquale Lops ; Thorsten Joachims ; Katrien Verbert ; Pablo Castells ; Zhenhua Dong ; Ben London. New York : Association for Computing Machinery, 2024. pp. 1195-1199

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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 ({\textquotedblleft}Ekstra Bladet{\textquotedblright}). 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.",

author = "Johannes Kruse and Kasper Lindskow and Saikishore Kalloori and Marco Polignano and Claudio Pomo and Abhishek Srivastava and Anshuk Uppal and \{Riis Andersen\}, Michael and Jes Frellsen",

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Kruse , J, Lindskow, K, Kalloori, S, Polignano, M, Pomo, C, Srivastava, A, Uppal, A, Riis Andersen, M & Frellsen, J 2024, RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations. in T Di Noia, P Lops, T Joachims, K Verbert, P Castells, Z Dong & B London (eds), RecSys 2024 - Proceedings of the 18th ACM Conference on Recommender Systems. Association for Computing Machinery, New York, pp. 1195-1199, 18th ACM Conference on Recommender Systems. Rec Sys 2024
, Bari, Italy, 14/10/2024. https://doi.org/10.1145/3640457.3687164

RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations. / Kruse , Johannes; Lindskow, Kasper; Kalloori, Saikishore et al.
RecSys 2024 - Proceedings of the 18th ACM Conference on Recommender Systems. ed. / Tommaso Di Noia; Pasquale Lops; Thorsten Joachims; Katrien Verbert; Pablo Castells; Zhenhua Dong; Ben London. New York: Association for Computing Machinery, 2024. p. 1195-1199.

Research output: Chapter in Book/Report/Conference proceeding › Conference abstract in proceedings › Research › peer-review

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AU - Kruse , Johannes

AU - Lindskow, Kasper

AU - Kalloori, Saikishore

AU - Polignano, Marco

AU - Pomo, Claudio

AU - Srivastava, Abhishek

AU - Uppal, Anshuk

AU - Riis Andersen, Michael

AU - Frellsen, Jes

N1 - Conference code: 18

PY - 2024

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N2 - 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.

AB - 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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M3 - Conference abstract in proceedings

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EP - 1199

BT - RecSys 2024 - Proceedings of the 18th ACM Conference on Recommender Systems

A2 - Di Noia, Tommaso

A2 - Lops, Pasquale

A2 - Joachims, Thorsten

A2 - Verbert, Katrien

A2 - Castells, Pablo

A2 - Dong, Zhenhua

A2 - London, Ben

PB - Association for Computing Machinery

CY - New York

Y2 - 14 October 2024 through 18 October 2024

ER -

Kruse J, Lindskow K, Kalloori S, Polignano M, Pomo C, Srivastava A et al. RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations. In Di Noia T, Lops P, Joachims T, Verbert K, Castells P, Dong Z, London B, editors, RecSys 2024 - Proceedings of the 18th ACM Conference on Recommender Systems. New York: Association for Computing Machinery. 2024. p. 1195-1199 doi: 10.1145/3640457.3687164