PBiLoss: Popularity-Aware Regularization to Improve Fairness in Graph-Based Recommender Systems
Learn to improve fairness in graph-based recommender systems using PBiLoss, a popularity-aware regularization technique, to reduce popularity bias and increase recommendation diversity.
- Implement PBiLoss in your graph-based recommender system to reduce popularity bias
- Use pre-processing methods to address popularity bias in the data
- Apply regularization techniques to the model to improve fairness
- Test and evaluate the performance of the system using metrics such as recommendation diversity and fairness
- Compare the results with and without PBiLoss to measure its effectiveness
Data scientists and engineers working on recommender systems can benefit from this technique to improve the fairness and personalization of their systems. This can be particularly useful for teams working on graph-based recommender systems, such as those using graph neural networks (GNNs).
💡 PBiLoss is a popularity-aware regularization technique that can help reduce popularity bias and improve fairness in graph-based recommender systems.
Improve fairness in graph-based recommender systems with PBiLoss! #recommendersystems #graphneuralnetworks #fairness
Key Takeaways
Learn to improve fairness in graph-based recommender systems using PBiLoss, a popularity-aware regularization technique, to reduce popularity bias and increase recommendation diversity.
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Abstract:
arXiv:2507.19067v2 Announce Type: replace-cross Abstract: Recommender systems based on graph neural networks (GNNs) have been proved to perform well on user-item interactions. However, they commonly suffer from popularity bias -- the tendency to over-recommend popular items -- resulting in less personalization, unfair exposure and lower recommendation diversity. Current solutions address popularity bias through different stages of the recommendation pipeline, including pre-processing methods tha
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