The extent to which popularity bias is propagated by media recommender systems is a current topic within the community, as is the uneven propagation among users with varying interests for niche items. Recent work focused on exactly this topic, with movies being the domain of interest. Later on, two different research teams reproduced the methodology in the domains of music and books, respectively. The results across the different domains diverge. In this paper, we reproduce the three studies and identify four aspects that are relevant in investigating the differences in results: data, algorithms, division of users in groups and evaluation strategy. We run a set of experiments in which we measure general popularity bias propagation and unfair treatment of certain users with various combinations of these aspects. We conclude that all aspects account to some degree for the divergence in results, and should be carefully considered in future studies. Further, we find that the divergence in findings can be in large part attributed to the choice of evaluation strategy.
ACM Transactions on Recommender Systems
Human-Centered Data Analytics

Daniil, S., Cuper, M., Liem, C., van Ossenbruggen, J., & Hollink, L. (2024). Reproducing popularity bias in recommendation: The effect of evaluation strategies. ACM Transactions on Recommender Systems, 2(1), 5:1–5:39. doi:10.1145/3637066