RecSys-Fairness: A Framework for Reducing Group Unfairness in Recommendations

In this study, we address the importance of promoting fairness in recommendation systems, which are highly susceptible to biases that can lead to unfair outcomes for different user groups. We developed a fairness algorithm aimed at mitigating these injustices, which was applied to the MovieLens dataset and analyzed based on the recommendations produced by the ALS (Alternating Least Squares) and NCF (Neural Collaborative Filtering) methods. Users were grouped by activity level, gender, and age, and the results demonstrated the effectiveness of the fairness algorithm in substantially reducing group unfairness (R_{grp}) across all tested configurations, without causing significant losses in recommendation accuracy, measured by the Root Mean Squared Error (RMSE). In particular, a reduction in group unfairness of up to 65.57% was observed in the ALS method. Additionally, we identified an optimal convergence of the fairness algorithm for an estimated number of matrices (h) between 10 and 15, suggesting an effective balance point between promoting fairness and maintaining precision in recommendations. In comparison with the available benchmarks, under identical experimental conditions, we managed to improve group unfairness reductions by approximately 6% (from 59.77% to 65.57%).