Evaluating Group Fairness in News Recommendations: A Comparative Study of Algorithms and Metrics-Reference-Cited by-同舟云学术

Evaluating Group Fairness in News Recommendations: A Comparative Study of Algorithms and Metrics

Published:2024-06-27 Issue: Volume: Page:337-346
ISSN:
Container-title:Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization
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Author:

Huebner Blake¹^ORCID,Kolb Thomas Elmar¹^ORCID,Neidhardt Julia¹^ORCID

Affiliation:

1. Institute of Information Systems Engineering, Research Unit of Data Science, CDL RecSys, TU Wien, Austria

Funder

Christian Doppler Forschungsgesellschaft

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3631700.3664897

Reference36 articles.

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3. User perception of differences in recommender algorithms

4. Michael D. Ekstrand, Mucun Tian, Ion Madrazo Azpiazu, Jennifer D. Ekstrand, Oghenemaro Anuyah, David McNeill, and Maria Soledad Pera. 2018. All The Cool Kids, How Do They Fit In?: Popularity and Demographic Biases in Recommender Evaluation and Effectiveness. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency. PMLR, 172–186. https://proceedings.mlr.press/v81/ekstrand18b.html ISSN: 2640-3498.

5. Golnoosh Farnadi Pigi Kouki Spencer K. Thompson Sriram Srinivasan and Lise Getoor. 2018. A Fairness-aware Hybrid Recommender System. https://doi.org/10.48550/arXiv.1809.09030 arXiv:1809.09030 [cs stat].