A plug-in contrastive loss that enforces monotonicity between numerical features and recommender outputs, via counterfactual sample synthesis, improves AUC, GAUC, and monotonicity.
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Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples
A plug-in contrastive loss that enforces monotonicity between numerical features and recommender outputs, via counterfactual sample synthesis, improves AUC, GAUC, and monotonicity.