Ad-Rec：应对推荐网络中协变量偏移的高级特征交互
Ad-Rec: Advanced Feature Interactions to Address Covariate-Shifts in Recommendation Networks
AI总结：
本文提出Ad-Rec网络，利用掩码变换器学习高阶特征交互以应对推荐系统中的协变量偏移，提高模型质量、加速收敛并减少训练时间。
摘要：
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常因用户行为和物品特征的演变而面临挑战，导致协变量偏移。有效的跨特征学习对于处理数据分布漂移和适应不断变化的用户行为至关重要。传统的特征交互技术在此背景下实现最优性能方面存在局限性。这项工作引入了Ad-Rec，一种利用特征交互技术来应对协变量偏移的高级网络。这有助于消除推荐任务中不相关的交互。Ad-Rec利用掩码变换器来学习高阶交叉特征，同时减轻数据分布漂移的影响。我们的方法提高了模型质量，加速了收敛，并减少了训练时间，以曲线下面积（AUC）指标衡量。我们通过全面的消融研究展示了Ad-Rec的可扩展性及其实现卓越模型质量的能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
