Being Diverse is Not Enough: Rethinking Diversity Evaluation to Meet Challenges of News Recommender Systems - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

Being Diverse is Not Enough: Rethinking Diversity Evaluation to Meet Challenges of News Recommender Systems

Résumé

Modern societies face many challenges, one of them is the rise of affective polarization over the last 4 decades. In an attempt to understand its reasons, many researchers have questioned the role of Social Media in general, and Recommender Systems (RS) in particular, on the emergence of these extreme behaviors. Diversity in News Recommender Systems (NRS) was quickly perceived as a major issue for the preservation of a healthy democratic debate. However, after more than 15 years of research in Artificial Intelligence on the subject, the understanding of the real impact of diversity in recommendations remains limited. Through a case analysis on the well-known MIND dataset, we propose a critique of the diversity-aware recommendation and evaluation approaches, and provide some take-home messages related to the need of adapted datasets, diversity metrics and analytical methodologies.
Fichier principal
Vignette du fichier
umap22adjunct-63.pdf (1.02 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03681454 , version 1 (30-05-2022)

Identifiants

Citer

Celina Treuillier, Sylvain Castagnos, Evan Dufraisse, Armelle Brun. Being Diverse is Not Enough: Rethinking Diversity Evaluation to Meet Challenges of News Recommender Systems. FairUMAP 2022 - Fairness in User Modeling, Adaptation and Personalization, Jul 2022, Barcelone, Spain. ⟨10.1145/3511047.3538030⟩. ⟨hal-03681454⟩
147 Consultations
311 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More