Exploring Cross-group Discrepancies in Calibrated Popularity for Accuracy/Fairness Trade-off Optimization

Popularity bias is an important issue in recommender systems, as it affects end-users, content creators, and content provider platforms alike. It can cause users to miss out on less popular items that would fit their preference, prevent new content creators from finding their audience, and force providers to pay higher royalties for serving expensive popular content. Over the past years, various approaches to mitigate popularity bias in recommender systems have been proposed. Among them, post-processing methods are widely accepted due to their versatility and ease of implementation. While previous studies have investigated the effects of different post-processing techniques on accuracy and fairness of recommendations, the influence of different algorithms on different user groups have not received much attention in this context. Addressing this research gap, we study the effect of a recent mitigation strategy, Calibrated Popularity, in conjunction with a selection of state-of-the-art recommender algorithms including BPR, ItemKNN, LightGCN, MultiVAE, and NeuMF. We show that these algorithms demonstrate different characteristics in terms of the trade-off between accuracy and fairness, both within and between various user groups defined by gender and inclination towards consumption of mainstream items. Finally, we demonstrate how these discrepancies can be exploited to achieve a more effective trade-off between utility and fairness of recommender systems