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

arXiv: https://arxiv.org/abs/1906.03776 · HTML: https://mlsanity.com/paper/1906.03776/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2019.

## Built on by (most cited first, 17 of 17)

- [A Survey on Graph Representation Learning Methods](https://mlsanity.com/paper/2204.01855/) (2022)
- [Click-Through Rate Prediction in Online Advertising: A Literature Review](https://mlsanity.com/paper/2202.10462/) (2022)
- [Multi-Task Fusion via Reinforcement Learning for Long-Term User Satisfaction in Recommender Systems](https://mlsanity.com/paper/2208.04560/) (2022)
- [User Response Prediction in Online Advertising](https://mlsanity.com/paper/2101.02342/) (2021)
- [Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction](https://mlsanity.com/paper/2105.08909/) (2021)
- [Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search](https://mlsanity.com/paper/2203.15542/) (2022)
- [Decision-Making Context Interaction Network for Click-Through Rate Prediction](https://mlsanity.com/paper/2301.12402/) (2023)
- [Attention Weighted Mixture of Experts with Contrastive Learning for Personalized Ranking in E-commerce](https://mlsanity.com/paper/2306.05011/) (2023)
- [Fragment and Integrate Network (FIN): A Novel Spatial-Temporal Modeling Based on Long Sequential Behavior for Online Food Ordering Click-Through Rate Prediction](https://mlsanity.com/paper/2308.15703/) (2023)
- [ContentCTR: Frame-level Live Streaming Click-Through Rate Prediction with Multimodal Transformer](https://mlsanity.com/paper/2306.14392/) (2023)
- [GACE: Learning Graph-Based Cross-Page Ads Embedding For Click-Through Rate Prediction](https://mlsanity.com/paper/2401.07445/) (2024)
- [Effective Two-Stage Knowledge Transfer for Multi-Entity Cross-Domain Recommendation](https://mlsanity.com/paper/2402.19101/) (2024)
- [Learning Multi-Branch Cooperation for Enhanced Click-Through Rate Prediction at Taobao](https://mlsanity.com/paper/2411.13057/) (2024)
- [Spatial-Temporal Deep Intention Destination Networks for Online Travel Planning](https://mlsanity.com/paper/2108.03989/) (2021)
- [Deep Group Interest Modeling of Full Lifelong User Behaviors for CTR Prediction](https://mlsanity.com/paper/2311.10764/) (2023)
- [FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate Prediction](https://mlsanity.com/paper/2407.18472/) (2024)
- [Time Matters: A Novel Real-Time Long- and Short-term User Interest Model for Click-Through Rate Prediction](https://mlsanity.com/paper/2511.06213/) (2025)

## Abstract (arXiv)

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.