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English Information

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Authors
# Name
1 João Costa(joaoppcosta@hotmail.com.br)
2 Daniel Ferreira(daniel.jcf@aluno.ufop.edu.br)
3 Anderson Ferreira(anderson.ferreira@ufop.edu.br)
4 Reinaldo Fortes(reifortes@ufop.edu.br)

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Reference
# Reference
1 Angwin, J., Larson, J., Mattu, S., and Kirchner, L. (2016). How we analyzed the COM-PAS recidivism algorithm. ProPublica.
2 Bao, X., Bergman, L., and Thompson, R. (2009). Stacking Recommendation Engines with Additional Meta-features. In ACM RecSys, pages 109–116, New York, NY, USA.
3 Boratto, L. et al. (2025). Popularity bias in recommender systems: The search for fairness in the long tail. Information, 16(2).
4 Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4):331–370.
5 Burke, R., Sonboli, N., and Ordonez-Gauger, A. (2018). Balanced neighborhoods for multi-sided fairness in recommendation. In Friedler, S. A. and Wilson, C., editors, Proceedings of the 1st Conference on Fairness, Accountability and Transparency, volume 81 of Proceedings of Machine Learning Research, pages 202–214. PMLR.
6 Deldjoo, Y., Jannach, D., Bellogin, A., Difonzo, A., and Zanzonelli, D. (2023). Fairness in recommender systems: Research landscape and future directions. User Modeling and User-Adapted Interaction.
7 Dinçer, B. T., Ounis, I., and Macdonald, C. (2014). Tackling biased baselines in the risk-sensitive evaluation of retrieval systems. In Proceedings of the 36th European Conference on Information Retrieval, pages 26–38. Springer.
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9 Fortes, R. S. (2022). Enhancing the multi-objective recommendation from three new perspectives: data characterization, risk-sensitiveness, and prioritization of the objectives.
10 Fu, Z., Xian, Y., Gao, R., Zhao, J., Huang, Q., Ge, Y., Xu, S., Geng, S., Shah, C., Zhang, Y., and de Melo, G. (2020). Fairness-aware explainable recommendation over knowledge graphs.
11 Herlocker, J., Konstan, J. A., Borchers, A., and Riedl, J. (2001). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), 19(1):5–53.
12 Ji, Y., Sun, A., Zhang, J., and Li, C. (2023). A critical study on data leakage in recommender system offline evaluation. ACM Transactions on Information Systems (TOIS), 41(3).
13 Klimashevskaia, A., Jannach, D., Elahi, M., and Trattner, C. (2024). A survey on popularity bias in recommender systems. User Modeling and User-Adapted Interaction.
14 Ma, H., Zhou, D., Liu, C., Lyu, M. R., and King, I. (2011). Recommender systems with social regularization. In Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM ’11, pages 287–296, New York, NY, USA. Association for Computing Machinery.
15 Pitoura, E., Stefanidis, K., and Koutrika, G. (2021). Fairness in rankings and recommendations: an overview. The VLDB Journal, pages 651–654.
16 Quadrana, M., Cremonesi, P., and Jannach, D. (2018). Sequence-aware recommender systems. ACM Computing Surveys (CSUR), 51(4):1–36.
17 Sill, J., Takacs, G., Mackey, L., and Lin, D. (2009). Feature-Weighted Linear Stacking. arXiv:0911.0460 [cs].
18 Wang, L., Bennett, P. N., and Collins-Thompson, K. (2012). Robust ranking models via risk-sensitive optimization.
19 Wang, Y., Chen, J., et al. (2023). A survey on fairness-aware recommender systems. Information Fusion.