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Recommender systems are widely used to provide personalized recommendationsto users. Recent research has shown that recommender systems may be subject todifferent types of biases, such as popularity bias, leading to an unevendistribution of recommendation exposure among producer groups. To mitigatethis, producer-centered fairness re-ranking (PFR) approaches have been proposedto ensure equitable recommendation utility across groups. However, theseapproaches overlook the harm they may cause to within-group individualsassociated with colder items, which are items with few or no interactions.  This study reproduces previous PFR approaches and shows that theysignificantly harm colder items, leading to a fairness gap for these items inboth advantaged and disadvantaged groups. Surprisingly, the unfair baserecommendation models were providing greater exposure opportunities to theseindividual cold items, even though at the group level, they appeared to beunfair. To address this issue, the study proposes an amendment to the PFRapproach that regulates the number of colder items recommended by the system.This modification achieves a balance between accuracy and producer fairnesswhile optimizing the selection of colder items within each group, therebypreventing or reducing harm to within-group individuals and augmenting thenovelty of all recommended items. The proposed method is able to register anincrease in sub-group fairness (SGF) from 0.3104 to 0.3782, 0.6156, and 0.9442while also improving group-level fairness (GF) (112% and 37% with respect tobase models and traditional PFR). Moreover, the proposed method achieves theseimprovements with minimal or no reduction in accuracy (or even an increasesometimes). We evaluate the proposed method on various recommendation datasetsand demonstrate promising results independent of the underlying model ordatasets.

Fairness for All: Investigating Harms to Within-Group Individuals in Producer Fairness Re-ranking Optimization -- A Reproducibility Study