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The article focuses on addressing fairness concerns in recommendation systems by proposing a novel type of fairness, where performance distributions across user groups should be similar. To achieve this, the study introduces a generative and adversarial training framework that ensures equivalent performance distributions across different user groups, aided by a dual curriculum learning strategy to smooth the training process.