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Recommendation models are vital in delivering personalized user experiencesby leveraging the correlation between multiple input features. However, deeplearning-based recommendation models often face challenges due to evolving userbehaviour and item features, leading to covariate shifts. Effectivecross-feature learning is crucial to handle data distribution drift andadapting to changing user behaviour. Traditional feature interaction techniqueshave limitations in achieving optimal performance in this context.  This work introduces Ad-Rec, an advanced network that leverages featureinteraction techniques to address covariate shifts. This helps eliminateirrelevant interactions in recommendation tasks. Ad-Rec leverages maskedtransformers to enable the learning of higher-order cross-features whilemitigating the impact of data distribution drift. Our approach improves modelquality, accelerates convergence, and reduces training time, as measured by theArea Under Curve (AUC) metric. We demonstrate the scalability of Ad-Rec and itsability to achieve superior model quality through comprehensive ablationstudies.

Ad-Rec: Advanced Feature Interactions to Address Covariate-Shifts in Recommendation Networks