Predicting click-through rates (CTR) is a fundamental task for Web applications, where a key issue is to devise effective models for feature interactions. Current methodologies predominantly concentrate on modeling feature interactions within an individual sample, while overlooking the potential cross-sample relationships that can serve as a reference context to enhance the prediction. To make up for such deficiency, this paper develops a Retrieval-Augmented Transformer (RAT), aiming to acquire fine-grained feature interactions within and across samples. By retrieving similar samples, we construct augmented input for each target sample. We then build Transformer layers with cascaded attention to capture both intra- and cross-sample feature interactions, facilitating comprehensive reasoning for improved CTR prediction while retaining efficiency. Extensive experiments on real-world datasets substantiate the effectiveness of RAT and suggest its advantage in long-tail scenarios. The code has been open-sourced at \url{https://github.com/YushenLi807/WWW24-RAT}.

翻译：预测点击率是Web应用中的一项基础任务，其关键问题在于设计有效的特征交互模型。当前主流方法主要集中于建模单个样本内的特征交互，却忽视了可作为参考上下文以增强预测的跨样本关系潜力。为弥补这一不足，本文提出检索增强Transformer，旨在获取样本内与样本间的细粒度特征交互。通过检索相似样本，我们为每个目标样本构建增强输入，进而通过级联注意力机制构建Transformer层，以捕获样本内与跨样本特征交互，从而在保持效率的同时促进对点击率预测的更全面推理。在真实数据集上的大量实验证实了RAT的有效性，并表明其在长尾场景中的优势。相关代码已在\url{https://github.com/YushenLi807/WWW24-RAT}开源。