# Representation Learning-Assisted Click-Through Rate Prediction

arXiv: https://arxiv.org/abs/1906.04365 · HTML: https://mlsanity.com/paper/1906.04365/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2019.

## Built on by (most cited first, 9 of 9)

- [Click-Through Rate Prediction in Online Advertising: A Literature Review](https://mlsanity.com/paper/2202.10462/) (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)
- [Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer](https://mlsanity.com/paper/2201.13311/) (2022)
- [Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search](https://mlsanity.com/paper/2203.15542/) (2022)
- [Click-through Rate Prediction with Auto-Quantized Contrastive Learning](https://mlsanity.com/paper/2109.13921/) (2021)
- [Learning List-wise Representation in Reinforcement Learning for Ads Allocation with Multiple Auxiliary Tasks](https://mlsanity.com/paper/2204.00888/) (2022)
- [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)
- [MindFuse: Towards GenAI Explainability in Marketing Strategy Co-Creation](https://mlsanity.com/paper/2512.04112/) (2025)

## Abstract (arXiv)

Click-through rate (CTR) prediction is a critical task in online advertising systems. Most existing methods mainly model the feature-CTR relationship and suffer from the data sparsity issue. In this paper, we propose DeepMCP, which models other types of relationships in order to learn more informative and statistically reliable feature representations, and in consequence to improve the performance of CTR prediction. In particular, DeepMCP contains three parts: a matching subnet, a correlation subnet and a prediction subnet. These subnets model the user-ad, ad-ad and feature-CTR relationship respectively. When these subnets are jointly optimized under the supervision of the target labels, the learned feature representations have both good prediction powers and good representation abilities. Experiments on two large-scale datasets demonstrate that DeepMCP outperforms several state-of-the-art models for CTR prediction.