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.

翻译：推荐模型通过利用多个输入特征之间的相关性，在提供个性化用户体验方面至关重要。然而，基于深度学习的推荐模型常因用户行为与物品特征的动态变化而面临挑战，导致协变量偏移。有效的交叉特征学习对于应对数据分布漂移并适应变化的用户行为至关重要。传统特征交互技术在此场景下实现最优性能存在局限性。本文提出Ad-Rec——一种利用特征交互技术应对协变量偏移的高级网络。该方法有助于消除推荐任务中无关的交互。Ad-Rec利用掩码变换器在学习高阶交叉特征的同时减轻数据分布漂移的影响。我们的方法提升了模型质量、加速了收敛并缩短了训练时间（以曲线下面积AUC衡量）。通过全面的消融研究，我们证明了Ad-Rec的可扩展性及其实现卓越模型质量的能力。