# Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction

## TL;DR

This paper introduces Deep Spatio-Temporal Neural Networks (DSTNs), a novel model that leverages auxiliary spatial and temporal ad data to significantly improve click-through rate prediction accuracy in online advertising.

## Contribution

The paper proposes DSTNs, a new neural network architecture that effectively fuses heterogeneous auxiliary ad data for enhanced CTR prediction, outperforming existing methods.

## Key findings

- DSTNs outperform state-of-the-art CTR prediction methods in offline tests.
- Deployment of DSTNs in Shenma Search increased online CTR significantly.
- A/B testing confirmed the effectiveness of DSTNs in real-world scenarios.

## Abstract

Click-through rate (CTR) prediction is a critical task in online advertising systems. A large body of research considers each ad independently, but ignores its relationship to other ads that may impact the CTR. In this paper, we investigate various types of auxiliary ads for improving the CTR prediction of the target ad. In particular, we explore auxiliary ads from two viewpoints: one is from the spatial domain, where we consider the contextual ads shown above the target ad on the same page; the other is from the temporal domain, where we consider historically clicked and unclicked ads of the user. The intuitions are that ads shown together may influence each other, clicked ads reflect a user's preferences, and unclicked ads may indicate what a user dislikes to certain extent. In order to effectively utilize these auxiliary data, we propose the Deep Spatio-Temporal neural Networks (DSTNs) for CTR prediction. Our model is able to learn the interactions between each type of auxiliary data and the target ad, to emphasize more important hidden information, and to fuse heterogeneous data in a unified framework. Offline experiments on one public dataset and two industrial datasets show that DSTNs outperform several state-of-the-art methods for CTR prediction. We have deployed the best-performing DSTN in Shenma Search, which is the second largest search engine in China. The A/B test results show that the online CTR is also significantly improved compared to our last serving model.