Analisando a Justiça de Grupo em Sistemas de Recomendação: Uma Avaliação de Estratégias de Filtragem e Agrupamentos de Usuários no Dataset MovieLens
Resumo
Este estudo investiga a equidade em sistemas de recomendação utilizando o dataset MovieLens, aplicando estratégias de filtragem colaborativa: ALS, KNN e NMF. Avaliamos a injustiça em diferentes configurações de agrupamento: Gênero, Idade, Avaliações e Aglomerativo. Os resultados indicam variações significativas de injustiça entre as estratégias, com o método Aglomerativo destacando-se por apresentar os maiores níveis de injustiça do grupo na maioria das abordagens. Esta análise sugere a necessidade de uma seleção cuidadosa da estratégia de filtragem e do método de agrupamento para promover sistemas de recomendação mais justos e inclusivos, destacando a importância de considerar múltiplas dimensões de injustiça na concepção destes sistemas.
Referências
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Hazrati, N. and Ricci, F. (2024). Choice models and recommender systems effects on users’ choices. User Model User-Adap Inter, 34(1):109–145.
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Leonhardt, J., Anand, A., and Khosla, M. (2018). User fairness in recommender systems. CoRR, abs/1807.06349.
Liu, S., Ge, Y., Xu, S., Zhang, Y., and Marian, A. (2022). Fairness-aware federated matrix factorization. In Proceedings of the 16th ACM Conference on Recommender Systems, RecSys ’22, page 168–178, New York, NY, USA. Association for Computing Machinery.
Liu, S., Sun, J., Deng, X., and et al. (2024). Towards platform profit-aware fairness in personalized recommendation. Int. J. Mach. Learn. & Cyber.
Pereira, F. S. F., Linhares, C. D. G., Ponciano, J. R., Gama, J., de Amo, S., and Oliveira, G. M. B. (2018). That’s my jam! uma análise temporal sobre a evolução das preferências dos usuários em uma rede social de músicas. In BraSNAM 2018 - VII Brazilian Workshop on Social Network Analysis and Mining, Porto Alegre, RS, Brasil. Sociedade Brasileira de Computação.
Rastegarpanah, B., Gummadi, K. P., and Crovella, M. (2019). Fighting fire with fire: Using antidote data to improve polarization and fairness of recommender systems. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, WSDM ’19. ACM.
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Souza, E., Lichtnow, D., and Gasparini, I. (2022). Estratégia de pós-processamento aplicada a um sistema de recomendação de artigos para a melhora da diversidade. In BraSNAM 2022 - XI Brazilian Workshop on Social Network Analysis and Mining, pages 216–221, Porto Alegre, RS, Brasil. Sociedade Brasileira de Computação.
Sudakov, V. A. and Titov, Y. P. (2023). Pandemic forecasting by machine learning in a decision support problem. Math Models Comput Simul, 15:520–528.
Vashistha, R., Noor, Z., and et al., S. D. (2023). Application of statistical machine learning in biomarker selection. Scientific Reports, 13:18331.
Xue, J., Yao, W., and Xiang, S. (2024). Machine learning embedded em algorithms for semiparametric mixture regression models. Computational Statistics.
Yao, S. and Huang, B. (2017). Beyond parity: Fairness objectives for collaborative filtering. CoRR, abs/1705.08804.
Zafar, M., Valera, I., Rodriguez, M., and Gummadi, K. P. (2017). Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment. In WWW ’17: Proceedings of the 26th International Conference on World Wide Web, pages 1171–1180.
Zehlike, M., Sühr, T., Baeza-Yates, R., Bonchi, F., Castillo, C., and Hajian, S. (2022). Fair top-k ranking with multiple protected groups. Processing & Management, 59(1):102707.
Bhargava, A., Bansal, A., and Goyal, V. (2022). Machine learning–based detection and sorting of multiple vegetables and fruits. Food Analytical Methods, 15:228–242.
Burke, R., Sonboli, N., and Ordonez-Gauger, A. (2018). Balanced neighborhoods for multi-sided fairness in recommendation. In FAT.
Celis, L. E., Deshpande, A., Kathuria, T., and Vishnoi, N. K. (2016). How to be fair and diverse? CoRR, abs/1610.07183.
Coelho, N. L. L., Figueiredo, T. F., and Figueiredo, R. (2023). Uso de programação linear inteira para geração e análise de agrupamentos de políticos da câmara dos deputados. In BraSNAM 2023 XII Brazilian Workshop on Social Network Analysis and Mining, João Pessoa, PB, Brasil. Sociedade Brasileira de Computação.
Dargan, S., Kumar, M., and et al., A. M. (2024). Handwriting-based gender classification using machine learning techniques. Multimedia Tools and Applications, 83:19871–19895.
Deldjoo, Y., Jannach, D., Bellogin, A., Difonzo, A., and Zanzonelli, D. (2024). Fairness in recommender systems: research landscape and future directions. User Modeling and User-Adapted Interaction, 34(1):59–108.
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R. S. (2011). Fairness through awareness. CoRR, abs/1104.3913.
Emirhüseyinoğlu, G., Shahhosseini, M., Hu, G., et al. (2023). Validation of scenario generation for decision-making using machine learning prediction models. Optim Lett.
Feng, X., Cai, Y., and Xin, R. (2023). Optimizing diabetes classification with a machine learning-based framework. BMC Bioinformatics, 24:428.
Harper, F. M. and Konstan, J. A. (2015). The movielens datasets: History and context. ACM Transactions on Interactive Intelligent Systems (TiiS), 5(4):Article 19.
Hazrati, N. and Ricci, F. (2024). Choice models and recommender systems effects on users’ choices. User Model User-Adap Inter, 34(1):109–145.
Kamishima, T. and Akaho, S. (2017). Considerations on recommendation independence for a find-good-items task. In Workshop on Responsible Recommendation.
Kamishima, T., Akaho, S., and Asoh, H. (2012). Enhancement of the neutrality in recommendation. In In Proc. of the 2nd Workshop on Human Decision Making in Recommender Systems, pages 8–14.
Kamishima, T., Akaho, S., Asoh, H., and Sakuma, J. (2018). Recommendation independence. 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 187–201. PMLR.
Leonhardt, J., Anand, A., and Khosla, M. (2018). User fairness in recommender systems. CoRR, abs/1807.06349.
Liu, S., Ge, Y., Xu, S., Zhang, Y., and Marian, A. (2022). Fairness-aware federated matrix factorization. In Proceedings of the 16th ACM Conference on Recommender Systems, RecSys ’22, page 168–178, New York, NY, USA. Association for Computing Machinery.
Liu, S., Sun, J., Deng, X., and et al. (2024). Towards platform profit-aware fairness in personalized recommendation. Int. J. Mach. Learn. & Cyber.
Pereira, F. S. F., Linhares, C. D. G., Ponciano, J. R., Gama, J., de Amo, S., and Oliveira, G. M. B. (2018). That’s my jam! uma análise temporal sobre a evolução das preferências dos usuários em uma rede social de músicas. In BraSNAM 2018 - VII Brazilian Workshop on Social Network Analysis and Mining, Porto Alegre, RS, Brasil. Sociedade Brasileira de Computação.
Rastegarpanah, B., Gummadi, K. P., and Crovella, M. (2019). Fighting fire with fire: Using antidote data to improve polarization and fairness of recommender systems. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, WSDM ’19. ACM.
Sikora, A., Zhang, T., and et al., D. M. (2023). Machine learning vs. traditional regression analysis for fluid overload prediction in the icu. Scientific Reports, 13:19654.
Souza, E., Lichtnow, D., and Gasparini, I. (2022). Estratégia de pós-processamento aplicada a um sistema de recomendação de artigos para a melhora da diversidade. In BraSNAM 2022 - XI Brazilian Workshop on Social Network Analysis and Mining, pages 216–221, Porto Alegre, RS, Brasil. Sociedade Brasileira de Computação.
Sudakov, V. A. and Titov, Y. P. (2023). Pandemic forecasting by machine learning in a decision support problem. Math Models Comput Simul, 15:520–528.
Vashistha, R., Noor, Z., and et al., S. D. (2023). Application of statistical machine learning in biomarker selection. Scientific Reports, 13:18331.
Xue, J., Yao, W., and Xiang, S. (2024). Machine learning embedded em algorithms for semiparametric mixture regression models. Computational Statistics.
Yao, S. and Huang, B. (2017). Beyond parity: Fairness objectives for collaborative filtering. CoRR, abs/1705.08804.
Zafar, M., Valera, I., Rodriguez, M., and Gummadi, K. P. (2017). Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment. In WWW ’17: Proceedings of the 26th International Conference on World Wide Web, pages 1171–1180.
Zehlike, M., Sühr, T., Baeza-Yates, R., Bonchi, F., Castillo, C., and Hajian, S. (2022). Fair top-k ranking with multiple protected groups. Processing & Management, 59(1):102707.
Publicado
21/07/2024
Como Citar
SANTOS, Rafael V. M.; COMARELA, Giovanni V..
Analisando a Justiça de Grupo em Sistemas de Recomendação: Uma Avaliação de Estratégias de Filtragem e Agrupamentos de Usuários no Dataset MovieLens. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 13. , 2024, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 13-23.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2024.1875.
