FaDeRS: Fairness and Depolarization in Recommender Systems
Abstract
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.
Keywords:
recommendation system, fairness, polarization
References
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G. Emirhüseyinoğlu, M. Shahhosseini, G. Hu, et al. 2023. Validation of scenario generation for decision-making using machine learning prediction models. Optim Lett 17 (2023), 520–528. DOI: 10.1007/s11590-023-02023-7
Felipe Faria, Álvaro Jr., and Luiz Merschmann. 2015. Predição de Rankings de Artistas por Meio de Regressão. In Anais do XI Simpósio Brasileiro de Sistemas de Informação (Goiânia). SBC, Porto Alegre, RS, Brasil, 95–102. DOI: 10.5753/sbsi.2015.5805
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Sorelle A. Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. 2016. On the (im)possibility of fairness. arXiv:1609.07236 [cs.CY] [link]
Avijit Ghosh, Ritam Dutt, and Christo Wilson. 2021. When Fair Ranking Meets Uncertain Inference. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’21). ACM, Virtual Event, Canada, 1033–1043. DOI: 10.1145/3404835.3462850
Wesckley Faria Gomes and Methanias Colaço. 2022. Applications of Artificial Intelligence for Auditing and Classification of Incongruent Descriptions in Public Procurement. In Proceedings of the XVIII Brazilian Symposium on Information Systems (Curitiba, Brazil) (SBSI ’22). Association for Computing Machinery, New York, NY, USA, Article 40, 8 pages. DOI: 10.1145/3535511.3535551
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Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma. 2018. Recommendation Independence. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (Proceedings of Machine Learning Research, Vol. 81), Sorelle A. Friedler and Christo Wilson (Eds.). PMLR, New York, NY, USA, 187–201. [link]
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Rodrigo Lima, Giovanni Comarella, Fabiano Belém, and Julio Reis. 2022. Justiça em Sistemas de Recomendação: Uma Análise de Técnicas de Regularização. In Anais do XXXVII Simpósio Brasileiro de Bancos de Dados (Búzios). SBC, Porto Alegre, RS, Brasil, 177–189. DOI: 10.5753/sbbd.2022.224352
Nícolas Terra Maia and Tiago Cruz de França. 2023. Recomendação de Conteúdo Baseada em Localização e Interesse: uma aplicação com o CidadeSocial. In Anais Estendidos do XXXVIII Simpósio Brasileiro de Bancos de Dados (BeloHorizonte/MG). SBC, Porto Alegre, RS, Brasil, 71–77. DOI: 10.5753/sbbd_estendido.2023.233535
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Published
2025-11-10
How to Cite
SANTOS, Rafael; COMARELA, Giovanni.
FaDeRS: Fairness and Depolarization in Recommender Systems. In: BRAZILIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB (WEBMEDIA), 31. , 2025, Rio de Janeiro/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 248-257.
DOI: https://doi.org/10.5753/webmedia.2025.15183.
