Improving Recommendation Fairness via Data Augmentation
This work addresses fairness issues in recommendation systems for users affected by biases, but it is incremental as it builds on existing data augmentation approaches without introducing a fundamentally new paradigm.
The paper tackles the problem of unfairness in collaborative filtering recommendation systems, where performance differs across user groups based on sensitive attributes, by proposing a data augmentation framework that balances imbalanced training data to improve fairness without needing predefined fairness metrics, achieving superior results in experiments on two real-world datasets.
Collaborative filtering based recommendation learns users' preferences from all users' historical behavior data, and has been popular to facilitate decision making. R Recently, the fairness issue of recommendation has become more and more essential. A recommender system is considered unfair when it does not perform equally well for different user groups according to users' sensitive attributes~(e.g., gender, race). Plenty of methods have been proposed to alleviate unfairness by optimizing a predefined fairness goal or changing the distribution of unbalanced training data. However, they either suffered from the specific fairness optimization metrics or relied on redesigning the current recommendation architecture. In this paper, we study how to improve recommendation fairness from the data augmentation perspective. The recommendation model amplifies the inherent unfairness of imbalanced training data. We augment imbalanced training data towards balanced data distribution to improve fairness. The proposed framework is generally applicable to any embedding-based recommendation, and does not need to pre-define a fairness metric. Extensive experiments on two real-world datasets clearly demonstrate the superiority of our proposed framework. We publish the source code at https://github.com/newlei/FDA.