Beyond Trade-offs: Unveiling Fairness-Constrained Diversity in News Recommender Systems
Pages 143 - 148
Abstract
Recommender Systems have played an important role in our daily lives for many years. However, it is only recently that their social impact has raised ethical issues and has thus been considered in the design of such systems. Particularly, News Recommender Systems (NRS) have a critical influence on individuals. NRS can provide overspecialized recommendations and enclose users into filter bubbles. Besides, NRS can influence users and make their original opinions diverge. Worse, they can orient users’ opinions towards more radical views. The literature has worked on these issues by leveraging diversity and fairness in the recommendation algorithms, but generally only one of these dimensions at a time. We propose to consider both diversity and fairness simultaneously to provide recommendations that are fair, diverse, and obviously accurate. To this end, we propose a novel recommendation framework, Accuracy-Diversity-Fairness (ADF), which considers that fairness is not at the expense of diversity. Concretely, fairness is approached as a constraint on diversity. Experiments highlight that constraining diversity by fairness remarkably contributes to providing recommendations 5 times more diverse than models of the literature, without any loss in accuracy.
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Index Terms
- Beyond Trade-offs: Unveiling Fairness-Constrained Diversity in News Recommender Systems
Recommendations
A Clustering Approach for Personalizing Diversity in Collaborative Recommender Systems
UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and PersonalizationMuch of the focus of recommender systems research has been on the accurate prediction of users' ratings for unseen items. Recent work has suggested that objectives such as diversity and novelty in recommendations are also important factors in the ...
A Novel Framework for Improving Recommender Diversity
International Workshop on Behavior and Social Informatics on Behavior and Social Computing - Volume 8178Recommender systems are being used to assist users in finding relevant items from a large set of alternatives in many online applications. However, while most research up to this point has focused on improving the accuracy of recommender systems, other ...
Effective methods for increasing aggregate diversity in recommender systems
In order to make a recommendation, a recommender system typically first predicts a user's ratings for items and then recommends a list of items to the user which have high predicted ratings. Quality of predictions is measured by accuracy, that is, how ...
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Published In
June 2024
338 pages
ISBN:9798400704338
DOI:10.1145/3627043
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Published: 22 June 2024
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UMAP '24: 32nd ACM Conference on User Modeling, Adaptation and Personalization
July 1 - 4, 2024
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