# Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction

arXiv: https://arxiv.org/abs/1911.00886 · HTML: https://mlsanity.com/paper/1911.00886/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2019.

## Built on by (most cited first, 4 of 4)

- [A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction](https://mlsanity.com/paper/2211.06684/) (2022)
- [Exploiting Session Information in BERT-based Session-aware Sequential Recommendation](https://mlsanity.com/paper/2204.10851/) (2022)
- [Click-Through Rate Prediction Using Graph Neural Networks and Online Learning](https://mlsanity.com/paper/2105.03811/) (2021)
- [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)

Improving the performance of click-through rate (CTR) prediction remains one of the core tasks in online advertising systems. With the rise of deep learning, CTR prediction models with deep networks remarkably enhance model capacities. In deep CTR models, exploiting users' historical data is essential for learning users' behaviors and interests. As existing CTR prediction works neglect the importance of the temporal signals when embed users' historical clicking records, we propose a time-aware attention model which explicitly uses absolute temporal signals for expressing the users' periodic behaviors and relative temporal signals for expressing the temporal relation between items. Besides, we propose a regularized adversarial sampling strategy for negative sampling which eases the classification imbalance of CTR data and can make use of the strong guidance provided by the observed negative CTR samples. The adversarial sampling strategy significantly improves the training efficiency, and can be co-trained with the time-aware attention model seamlessly. Experiments are conducted on real-world CTR datasets from both in-station and out-station advertising places.