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.

本文介绍了一种名为Ad-Rec的高级网络，它利用特征交互技术来解决协变量偏移问题，在推荐任务中消除了不相关的交互，通过遮蔽变换器实现了学习高阶交叉特征，并减轻数据分布漂移的影响。我们的方法通过综合消融研究证明了Ad-Rec的可扩展性和卓越的模型质量，改进了模型质量，加快了收敛速度并减少了训练时间，这些都通过AUC度量得到了验证。

Ad-Rec: 处理推荐网络的协变量偏移的高级特征交互