Ad-Rec: Advanced Feature Interactions to Address Covariate-Shifts in
  Recommendation Networks
Muhammad Adnan Yassaman Ebrahimzadeh Maboud Divya Mahajan Prashant J. Nair
Recommendation models are vital in delivering personalized user experiences
by leveraging the correlation between multiple input features. However, deep
learning-based recommendation models often face challenges due to evolving user
behaviour and item features, leading to covariate shifts. Effective
cross-feature learning is crucial to handle data distribution drift and
adapting to changing user behaviour. Traditional feature interaction techniques
have limitations in achieving optimal performance in this context.
  This work introduces Ad-Rec, an advanced network that leverages feature
interaction techniques to address covariate shifts. This helps eliminate
irrelevant interactions in recommendation tasks. Ad-Rec leverages masked
transformers to enable the learning of higher-order cross-features while
mitigating the impact of data distribution drift. Our approach improves model
quality, accelerates convergence, and reduces training time, as measured by the
Area Under Curve (AUC) metric. We demonstrate the scalability of Ad-Rec and its
ability to achieve superior model quality through comprehensive ablation
studies.