Fairness and Popularity Bias in Recommender Systems: an Empirical Evaluation. Musto, C., Lops, P., & Semeraro, G. In AIxIA 2021 Discussion Papers, 20th International Conference Italian Association for Artificial Intelligence, 2021. ![Fairness and Popularity Bias in Recommender Systems: an Empirical Evaluation [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex In this paper, we present the results of an empirical evaluation investigating how recommendation algorithms are affected by popularity bias. Popularity bias makes more popular items to be recommended more frequently than less popular ones, thus it is one of the most relevant issues that limits the fairness of recommender systems. In particular, we define an experimental protocol based on two state-of-theart datasets containing users’ preferences on movies and books and three different recommendation paradigms, i.e., collaborative filtering, content-based filtering and graph-based algorithms. In order to evaluate the overall fairness of the recommendations we use well-known metrics such as Catalogue Coverage, Gini Index and Group Average Popularity (ΔGAP). The goal of this paper is: (i) to provide a clear picture of how recommendation techniques are affected by popularity bias; (ii) to trigger further research in the area aimed to introduce methods to mitigate or reduce biases in order to provide fairer recommendations.
@inproceedings{musto_fairness_2021,
title = {Fairness and {Popularity} {Bias} in {Recommender} {Systems}: an {Empirical} {Evaluation}},
url = {https://ceur-ws.org/Vol-3078/paper-69.pdf},
abstract = {In this paper, we present the results of an empirical evaluation investigating how recommendation algorithms are affected by popularity bias. Popularity bias makes more popular items to be recommended more frequently than less popular ones, thus it is one of the most relevant issues that limits the fairness of recommender systems. In particular, we define an experimental protocol based on two state-of-theart datasets containing users’ preferences on movies and books and three different recommendation paradigms, i.e., collaborative filtering, content-based filtering and graph-based algorithms. In order to evaluate the overall fairness of the recommendations we use well-known metrics such as Catalogue Coverage, Gini Index and Group Average Popularity (ΔGAP). The goal of this paper is: (i) to provide a clear picture of how recommendation techniques are affected by popularity bias; (ii) to trigger further research in the area aimed to introduce methods to mitigate or reduce biases in order to provide fairer recommendations.},
language = {en},
booktitle = {{AIxIA} 2021 {Discussion} {Papers}, 20th {International} {Conference} {Italian} {Association} for {Artificial} {Intelligence}},
author = {Musto, Cataldo and Lops, Pasquale and Semeraro, Giovanni},
year = {2021},
keywords = {⛔ No DOI found},
}
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