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

by   Giovanni Pellegrini, et al.

Recommender systems are widely used to provide personalized recommendations to users. Recent research has shown that recommender systems may be subject to different types of biases, such as popularity bias, leading to an uneven distribution of recommendation exposure among producer groups. To mitigate this, producer-centered fairness re-ranking (PFR) approaches have been proposed to ensure equitable recommendation utility across groups. However, these approaches overlook the harm they may cause to within-group individuals associated with colder items, which are items with few or no interactions. This study reproduces previous PFR approaches and shows that they significantly harm colder items, leading to a fairness gap for these items in both advantaged and disadvantaged groups. Surprisingly, the unfair base recommendation models were providing greater exposure opportunities to these individual cold items, even though at the group level, they appeared to be unfair. To address this issue, the study proposes an amendment to the PFR approach that regulates the number of colder items recommended by the system. This modification achieves a balance between accuracy and producer fairness while optimizing the selection of colder items within each group, thereby preventing or reducing harm to within-group individuals and augmenting the novelty of all recommended items. The proposed method is able to register an increase in sub-group fairness (SGF) from 0.3104 to 0.3782, 0.6156, and 0.9442 while also improving group-level fairness (GF) (112 base models and traditional PFR). Moreover, the proposed method achieves these improvements with minimal or no reduction in accuracy (or even an increase sometimes). We evaluate the proposed method on various recommendation datasets and demonstrate promising results independent of the underlying model or datasets.


page 11

page 14


Joint Multisided Exposure Fairness for Recommendation

Prior research on exposure fairness in the context of recommender system...

Experiments on Generalizability of User-Oriented Fairness in Recommender Systems

Recent work in recommender systems mainly focuses on fairness in recomme...

Equal Experience in Recommender Systems

We explore the fairness issue that arises in recommender systems. Biased...

Addressing the Extreme Cold-Start Problem in Group Recommendation

The task of recommending items to a group of users, a.k.a. group recomme...

Enumerating Fair Packages for Group Recommendations

In package recommendations, a set of items is regarded as a unified pack...

Optimizing generalized Gini indices for fairness in rankings

There is growing interest in designing recommender systems that aim at b...

Fairness Vs. Personalization: Towards Equity in Epistemic Utility

The applications of personalized recommender systems are rapidly expandi...

Please sign up or login with your details

Forgot password? Click here to reset