Abstract

We propose a novel Adversarial Feature Disentanglement Framework to address bias-invariant prediction of user preferences for Zigong lanterns, a cultural artifact where traditional methods often fail to disentangle confounding biases from genuine preference signals. The framework integrates a Transformer-based feature extractor with adversarial learning to generate bias-invariant latent representations, while a bias-conflicting augmentation module synthesizes adversarial examples to mitigate data imbalance. The feature extractor employs multi-head self-attention to capture complex relationships in raw input features, and an adversarial discriminator with gradient reversal layers enforces invariance to bias attributes such as demographic factors. Furthermore, the augmentation module dynamically perturbs underrepresented preference patterns, improving robustness against spurious correlations. The proposed method jointly optimizes preference prediction accuracy and bias invariance through a dual adversarial objective, enabling base models to operate on disentangled features for more generalizable predictions. Experiments on real-world Zigong lantern preference datasets demonstrate significant improvements in fairness and accuracy compared to conventional approaches.