# AT4CTR: Auxiliary Match Tasks for Enhancing Click-Through Rate Prediction

arXiv: https://arxiv.org/abs/2312.06683 · HTML: https://mlsanity.com/paper/2312.06683/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2023.

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

- [Alignment Quality Index (AQI) : Beyond Refusals: AQI as an Intrinsic Alignment Diagnostic via Latent Geometry, Cluster Divergence, and Layer wise Pooled Representations](https://mlsanity.com/paper/2506.13901/) (2025)
- [DETONATE: A Benchmark for Text-to-Image Alignment and Kernelized Direct Preference Optimization](https://mlsanity.com/paper/2506.14903/) (2025)
- [GIST: Cross-Domain Click-Through Rate Prediction via Guided Content-Behavior Distillation](https://mlsanity.com/paper/2507.05142/) (2025)
- [Generative Long-term User Interest Modeling for Click-Through Rate Prediction](https://mlsanity.com/paper/2605.15905/) (2026)

## Abstract (arXiv)

Click-through rate (CTR) prediction is a vital task in industrial recommendation systems. Most existing methods focus on the network architecture design of the CTR model for better accuracy and suffer from the data sparsity problem. Especially in industrial recommendation systems, the widely applied negative sample down-sampling technique due to resource limitation worsens the problem, resulting in a decline in performance. In this paper, we propose Auxiliary Match Tasks for enhancing Click-Through Rate prediction accuracy (AT4CTR) by alleviating the data sparsity problem. Specifically, we design two match tasks inspired by collaborative filtering to enhance the relevance modeling between user and item. As the "click" action is a strong signal which indicates the user's preference towards the item directly, we make the first match task aim at pulling closer the representation between the user and the item regarding the positive samples. Since the user's past click behaviors can also be treated as the user him/herself, we apply the next item prediction as the second match task. For both the match tasks, we choose the InfoNCE as their loss function. The two match tasks can provide meaningful training signals to speed up the model's convergence and alleviate the data sparsity. We conduct extensive experiments on one public dataset and one large-scale industrial recommendation dataset. The result demonstrates the effectiveness of the proposed auxiliary match tasks. AT4CTR has been deployed in the real industrial advertising system and has gained remarkable revenue.