Automatic Feature Fairness in Recommendation via Adversaries
Hengchang Hu Yiming Cao Zhankui He Samson Tan Min-Yen Kan
Fairness is a widely discussed topic in recommender systems, but its
practical implementation faces challenges in defining sensitive features while
maintaining recommendation accuracy. We propose feature fairness as the
foundation to achieve equitable treatment across diverse groups defined by
various feature combinations. This improves overall accuracy through balanced
feature generalizability. We introduce unbiased feature learning through
adversarial training, using adversarial perturbation to enhance feature
representation. The adversaries improve model generalization for
under-represented features. We adapt adversaries automatically based on two
forms of feature biases: frequency and combination variety of feature values.
This allows us to dynamically adjust perturbation strengths and adversarial
training weights. Stronger perturbations are applied to feature values with
fewer combination varieties to improve generalization, while higher weights for
low-frequency features address training imbalances. We leverage the Adaptive
Adversarial perturbation based on the widely-applied Factorization Machine
(AAFM) as our backbone model. In experiments, AAFM surpasses strong baselines
in both fairness and accuracy measures. AAFM excels in providing item- and
user-fairness for single- and multi-feature tasks, showcasing their versatility
and scalability. To maintain good accuracy, we find that adversarial
perturbation must be well-managed: during training, perturbations should not
overly persist and their strengths should decay.