  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}.
