Integrating the proposed methods with other debiasing techniques, such as data augmentation or adversarial training, can enhance the performance of the model in real-world recommender systems:
Data Augmentation:
Data augmentation techniques can be used to generate synthetic data points that reflect diverse neighborhood interactions. By augmenting the training data with variations of user-item pairs and their corresponding treatments, the model can learn to generalize better to different neighborhood structures.
Adversarial Training:
Adversarial training can be employed to enhance the robustness of the model against potential biases in the neighborhood effect. By training the model to resist adversarial attacks that aim to introduce biases in the neighborhood representation, the model can learn more robust and unbiased representations.
Ensemble Approaches:
Combining the proposed methods with ensemble techniques, such as model averaging or stacking, can further improve the model's performance. By leveraging the diversity of multiple debiasing methods, the ensemble model can capture a broader range of neighborhood effects and enhance the overall recommendation accuracy.
By integrating these complementary debiasing techniques with the proposed methods, recommender systems can achieve more robust and accurate performance in real-world scenarios.