Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations

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Tsintzou, V., Pitoura, E., Tsaparas, P.: Bias disparity in recommendation systems. arXiv e-prints p. arXiv:1811.01461 (11 2018)

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Dacrema, M.F., Cremonesi, P., Jannach, D.: Are we really making much progress? A worrying analysis of recent neural recommendation approaches. In: Proceedings of the 13th ACM Conference on Recommender Systems, RecSys 2019, Copenhagen, Denmark, September 16-20, 2019. pp. 101–109. ACM (2019). https://doi.org/10.1145/3298689.3347058, https://doi.org/10.1145/3298689.3347058

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Rastegarpanah, B., Gummadi, K.P., Crovella, M.: 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 2019, Melbourne, VIC, Australia, February 11-15, 2019. pp. 231–239. ACM (2019). https://doi.org/10.1145/3289600.3291002, https://doi.org/10.1145/3289600.3291002

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Ekstrand, M.D., Das, A., Burke, R., Diaz, F.: Fairness and discrimination in information access systems. CoRR abs/2105.05779

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Frisch, G., Leger, J.B., Grandvalet, Y.: Co-clustering for fair recommendation. Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (2021), https://hal.archives-ouvertes.fr/hal-03239856

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Li, Y., Chen, H., Fu, Z., Ge, Y., Zhang, Y.: User-oriented fairness in recommendation. In: WWW ’21: The Web Conference 2021. pp. 624–632. ACM / IW3C2 (2021). https://doi.org/10.1145/3442381.3449866, https://doi.org/10.1145/3442381.3449866

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Li, Y., Chen, H., Xu, S., Ge, Y., Zhang, Y.: Towards personalized fairness based on causal notion. pp. 1054–1063. Association for Computing Machinery (2021). https://doi.org/10.1145/3404835.3462966, https://doi.org/10.1145/3404835.3462966