Implementation of Fairness Measures: A Case Study in the Cultural Context for Different Strategies in Recommendation Systems
DOI:
https://doi.org/10.5753/isys.2024.4214Keywords:
Recommender Systems, Fairness, Individual Fairness, Group FairnessAbstract
This study analyzes equity in recommendation systems, specifically in a musical context, focusing on understanding disparities across different filtering strategies. Through the implementation of equity metrics, we investigate individual and group injustices among various recommendation approaches. The results reveal significant variations in the fairness applied by the examined strategies, highlighting the complexity of achieving equity in these systems. We conclude that a detailed analysis of justice in recommendation systems is crucial for identifying and understanding existing disparities, contributing to the future development of more equitable solutions.
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Barocas, S. and Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104(3):671–732.
Berk, R., Heidari, H., Jabbari, S., Joseph, M., Kearns, M. J., Morgenstern, J., Neel, S., and Roth, A. (2017). A convex framework for fair regression. CoRR, abs/1706.02409.
Beutel, A., Chi, E. H., Cheng, Z., Pham, H., and Anderson, J. (2017). Beyond globally optimal: Focused learning for improved recommendations. In Proceedings of the 26th International Conference on WWW 2017, Perth, Australia, April 3-7, 2017.
Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User modeling and user-adapted interaction, 12(4):331–370.
Burke, R. (2007). Hybrid web recommender systems. In The adaptive web, pages 377–408. Springer.
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R. (2012). Fairness through awareness. In Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, ITCS ’12, page 214–226, New York, NY, USA. Association for Computing Machinery.
Harper, F. M. and Konstan, J. A. (2015). The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis), 5(4):1–19.
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.
Lops, P., de Gemmis, M., and Semeraro, G. (2011). Content-based recommender systems: State of the art and trends. Recommender systems handbook, pages 73–105.
Su, X. and Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques. Advances in artificial intelligence, 2009.
Zafar, M. B., Valera, I., Gomez Rodriguez, M., and Gummadi, K. P. (2017). Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment. In Proceedings of the 26th International Conference on World Wide Web, WWW '17, page 1171–1180, Republic and Canton of Geneva, CHE. International World Wide Web Conferences Steering Committee.
Zehlike, M., Su¨hr, 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.
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