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Advancing Fairness in Graph Collaborative Filtering: A Comprehensive Framework for Theoretical Formalization and Enhanced Mitigation Techniques

Advancing Fairness in Graph Collaborative Filtering: A Comprehensive Framework for Theoretical Formalization and Enhanced Mitigation Techniques

Practical Solutions for Fairness in Recommender Systems

Addressing Unfairness in Recommendations

Recommender systems are powerful tools for personalized suggestions, but concerns about trustworthiness and fairness have arisen. To tackle unfairness, algorithms have been developed and categorized into pre-processing, in-processing, and post-processing approaches.

Enhanced Mitigation Techniques

A detailed approach has been proposed to address the limitations of previous fairness-aware graph collaborative filtering (GCF) methods. This includes theoretical formalization of sampling policies, augmented graph integration in graph neural networks (GNNs), and the introduction of FA4GCF (Fair Augmentation for Graph Collaborative Filtering) – a versatile, publicly available tool.

Expanded Scope of Evaluation

The proposed method significantly expands the scope of evaluation compared to previous studies by replacing datasets and extending the experimental evaluation to include datasets from diverse domains such as movies, fashion, and points of interest in cities. Consistent pre-processing steps are applied, and a temporal user-based splitting strategy is adopted.

Impact on Different Models and Datasets

The results reveal that fairness mitigation techniques have varying impacts across different models and datasets, with different sampling policies exhibiting varying effectiveness. The evaluation covered diverse datasets, models, and fairness metrics, providing a more detailed assessment of the algorithmโ€™s effectiveness.

Establishing a Robust Framework

This paper provides valuable insights into the complexities of fairness mitigation in GCF and establishes a robust framework for future research in the recommender systems field.

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