Fairness in Recommender Systems

@inproceedings{Ekstrand2022FairnessIR,
  title={Fairness in Recommender Systems},
  author={Michael D. Ekstrand and Anubrata Das and Robin D. Burke and Fernando Diaz},
  booktitle={Recommender Systems Handbook},
  year={2022}
}
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2 Citations
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2 Citations

Building Human Values into Recommender Systems: An Interdisciplinary Synthesis

This paper collects a set of values that seem most relevant to recommender systems operating across different domains, then examines them from the perspectives of current industry practice, measurement, product design, and policy approaches.

A Comparative Analysis of Bias Amplification in Graph Neural Network Approaches for Recommender Systems

This paper aims to comprehensively study the bias amplification issue through a literature review and an analysis of the behavior against biases of different GNN-based algorithms compared to state-of-the-art methods.