Counterfactual Graph Augmentation for Consumer Unfairness Mitigation in Recommender Systems

Author:

Boratto Ludovico1ORCID,Fabbri Francesco2ORCID,Fenu Gianni1ORCID,Marras Mirko1ORCID,Medda Giacomo1ORCID

Affiliation:

1. University of Cagliari, Cagliari, Italy

2. Spotify, Barcelona, Spain

Publisher

ACM

Reference38 articles.

1. Chirag Agarwal , Himabindu Lakkaraju , and Marinka Zitnik . 2021 . Towards a unified framework for fair and stable graph representation learning . In Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, UAI 2021, Vol. 161 . AUAI Press, 2114--2124. Chirag Agarwal, Himabindu Lakkaraju, and Marinka Zitnik. 2021. Towards a unified framework for fair and stable graph representation learning. In Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, UAI 2021, Vol. 161. AUAI Press, 2114--2124.

2. Fairness metrics and bias mitigation strategies for rating predictions

3. Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations

4. DeepFair: Deep Learning for Improving Fairness in Recommender Systems

5. Ludovico Boratto , Gianni Fenu , Mirko Marras , and Giacomo Medda . 2022. Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations . In Advances in Information Retrieval . Springer International Publishing , Cham , 552--566. Ludovico Boratto, Gianni Fenu, Mirko Marras, and Giacomo Medda. 2022. Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations. In Advances in Information Retrieval. Springer International Publishing, Cham, 552--566.

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