# Deep CTR Prediction in Display Advertising

arXiv: https://arxiv.org/abs/1609.06018 · HTML: https://mlsanity.com/paper/1609.06018/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2016.

## Built on by (most cited first, 30 of 31)

- [Click-Through Rate Prediction in Online Advertising: A Literature Review](https://mlsanity.com/paper/2202.10462/) (2022)
- [Enhancing Taobao Display Advertising with Multimodal Representations: Challenges, Approaches and Insights](https://mlsanity.com/paper/2407.19467/) (2024)
- [User Response Prediction in Online Advertising](https://mlsanity.com/paper/2101.02342/) (2021)
- [A Hybrid Bandit Model with Visual Priors for Creative Ranking in Display Advertising](https://mlsanity.com/paper/2102.04033/) (2021)
- [HeterPS: Distributed Deep Learning With Reinforcement Learning Based Scheduling in Heterogeneous Environments](https://mlsanity.com/paper/2111.10635/) (2021)
- [Large Language Models are Not Stable Recommender Systems](https://mlsanity.com/paper/2312.15746/) (2023)
- [Efficient Optimal Selection for Composited Advertising Creatives with Tree Structure](https://mlsanity.com/paper/2103.01453/) (2021)
- [Online Advertising Revenue Forecasting: An Interpretable Deep Learning Approach](https://mlsanity.com/paper/2111.08840/) (2021)
- [Nonuniform Negative Sampling and Log Odds Correction with Rare Events Data](https://mlsanity.com/paper/2110.13048/) (2021)
- [Confidence-Aware Multi-Field Model Calibration](https://mlsanity.com/paper/2402.17655/) (2024)
- [Retrievable Domain-Sensitive Feature Memory for Multi-Domain Recommendation](https://mlsanity.com/paper/2405.12892/) (2024)
- [Field Matters: A Lightweight LLM-enhanced Method for CTR Prediction](https://mlsanity.com/paper/2505.14057/) (2025)
- [Hybrid CNN Based Attention with Category Prior for User Image Behavior Modeling](https://mlsanity.com/paper/2205.02711/) (2022)
- [Improved Online Learning Algorithms for CTR Prediction in Ad Auctions](https://mlsanity.com/paper/2403.00845/) (2024)
- [Macro Graph of Experts for Billion-Scale Multi-Task Recommendation](https://mlsanity.com/paper/2506.10520/) (2025)
- [Ad Creative Discontinuation Prediction with Multi-Modal Multi-Task Neural Survival Networks](https://mlsanity.com/paper/2204.11588/) (2022)
- [DM2S2: Deep Multi-Modal Sequence Sets with Hierarchical Modality Attention](https://mlsanity.com/paper/2209.03126/) (2022)
- [Pairwise Ranking Losses of Click-Through Rates Prediction for Welfare Maximization in Ad Auctions](https://mlsanity.com/paper/2306.01799/) (2023)
- [Cross-Element Combinatorial Selection for Multi-Element Creative in Display Advertising](https://mlsanity.com/paper/2307.01593/) (2023)
- [GACE: Learning Graph-Based Cross-Page Ads Embedding For Click-Through Rate Prediction](https://mlsanity.com/paper/2401.07445/) (2024)
- [Generative Modeling with Multi-Instance Reward Learning for E-commerce Creative Optimization](https://mlsanity.com/paper/2508.09730/) (2025)
- [M2FN: Multi-step Modality Fusion for Advertisement Image Assessment](https://mlsanity.com/paper/2102.00441/) (2021)
- [AI-Driven Contextual Advertising: A Technology Report and Implication Analysis](https://mlsanity.com/paper/2205.00911/) (2022)
- [Improving Prediction Performance and Model Interpretability through Attention Mechanisms from Basic and Applied Research Perspectives](https://mlsanity.com/paper/2303.14116/) (2023)
- [Beyond Rankings: Exploring the Impact of SERP Features on Organic Click-through Rates](https://mlsanity.com/paper/2306.01785/) (2023)
- [AdSEE: Investigating the Impact of Image Style Editing on Advertisement Attractiveness](https://mlsanity.com/paper/2309.08159/) (2023)
- [PRECTR: A Synergistic Framework for Integrating Personalized Search Relevance Matching and CTR Prediction](https://mlsanity.com/paper/2503.18395/) (2025)
- [Toward a benchmark for CTR prediction in online advertising: datasets, evaluation protocols and perspectives](https://mlsanity.com/paper/2512.01179/) (2025)
- [DAIAN: Deep Adaptive Intent-Aware Network for CTR Prediction in Trigger-Induced Recommendation](https://mlsanity.com/paper/2602.13971/) (2026)
- [Decoding the Hook: A Multimodal LLM Framework for Analyzing the Hooking Period of Video Ads](https://mlsanity.com/paper/2602.22299/) (2026)

## Abstract (arXiv)

Click through rate (CTR) prediction of image ads is the core task of online display advertising systems, and logistic regression (LR) has been frequently applied as the prediction model. However, LR model lacks the ability of extracting complex and intrinsic nonlinear features from handcrafted high-dimensional image features, which limits its effectiveness. To solve this issue, in this paper, we introduce a novel deep neural network (DNN) based model that directly predicts the CTR of an image ad based on raw image pixels and other basic features in one step. The DNN model employs convolution layers to automatically extract representative visual features from images, and nonlinear CTR features are then learned from visual features and other contextual features by using fully-connected layers. Empirical evaluations on a real world dataset with over 50 million records demonstrate the effectiveness and efficiency of this method.