FaDeRS: Fairness and Depolarization in Recommender Systems - Santos, Rafael; Comarela, Giovanni (Proceedings of the Brazilian Symposium on Multimedia and the Web (WebMedia); 2025: Proceedings of the 31st Brazilian Symposium on Multimedia and the Web, 2025)
Recommender systems have become fundamental in modern society as they help navigate the vast amount of available data, enabling users to find information, products, or services more efficiently and personally. They directly impact how people consume data, goods, and resources. Recommender systems often lack fairness and diversity, resulting in unfair services and increased preference polarization. Solution: This work presents FaDeRS (Fairness and Depolarization in Recommender Systems), an approach aimed at increasing fairness and diversity in recommender systems. FaDeRS adjusts predictions through controlled perturbations and optimization to mitigate individual unfairness and polarization without modifying the input data. The research is related to socio-technical theory, addressing one of the socio-algorithmic problems, algorithmic discrimination. We consider a specific set of approaches to encode fair behaviors. The research applied a quantitative method with experimentation using two datasets in distinct contexts, implementing a post-processing algorithm based on the Simulated Annealing meta-heuristic. The proposed method demonstrated significant reductions in polarization (up to 78.64%) and individual unfairness (up to 33.97%), with only a small increase in the Root Mean Square Error (RMSE), indicating an improvement in the socially desirable qualities of the systems without unduly sacrificing accuracy. Notably, FaDeRS consistently outperformed a relevant benchmark methodology across both evaluated datasets. The main contribution is a mechanism that balances personalization and fairness, simultaneously addressing polarization and individual unfairness from the items’ perspective, promoting a fairer and more diverse approach to recommendation.