# Category-Specific CNN for Visual-aware CTR Prediction at JD.com

arXiv: https://arxiv.org/abs/2006.10337 · HTML: https://mlsanity.com/paper/2006.10337/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2020.

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

- [A Hybrid Bandit Model with Visual Priors for Creative Ranking in Display Advertising](https://mlsanity.com/paper/2102.04033/) (2021)
- [Efficient Optimal Selection for Composited Advertising Creatives with Tree Structure](https://mlsanity.com/paper/2103.01453/) (2021)
- [Boost CTR Prediction for New Advertisements via Modeling Visual Content](https://mlsanity.com/paper/2209.11727/) (2022)
- [Attention Weighted Mixture of Experts with Contrastive Learning for Personalized Ranking in E-commerce](https://mlsanity.com/paper/2306.05011/) (2023)
- [Hybrid CNN Based Attention with Category Prior for User Image Behavior Modeling](https://mlsanity.com/paper/2205.02711/) (2022)
- [Generative Modeling with Multi-Instance Reward Learning for E-commerce Creative Optimization](https://mlsanity.com/paper/2508.09730/) (2025)
- [AdSEE: Investigating the Impact of Image Style Editing on Advertisement Attractiveness](https://mlsanity.com/paper/2309.08159/) (2023)
- [Helen: Optimizing CTR Prediction Models with Frequency-wise Hessian Eigenvalue Regularization](https://mlsanity.com/paper/2403.00798/) (2024)
- [PinDCO: Whole-Page Aware Dynamic Creative Optimization at Scale](https://mlsanity.com/paper/2609.11943/) (2026)

## Abstract (arXiv)

As one of the largest B2C e-commerce platforms in China, JD com also powers a leading advertising system, serving millions of advertisers with fingertip connection to hundreds of millions of customers. In our system, as well as most e-commerce scenarios, ads are displayed with images.This makes visual-aware Click Through Rate (CTR) prediction of crucial importance to both business effectiveness and user experience. Existing algorithms usually extract visual features using off-the-shelf Convolutional Neural Networks (CNNs) and late fuse the visual and non-visual features for the finally predicted CTR. Despite being extensively studied, this field still face two key challenges. First, although encouraging progress has been made in offline studies, applying CNNs in real systems remains non-trivial, due to the strict requirements for efficient end-to-end training and low-latency online serving. Second, the off-the-shelf CNNs and late fusion architectures are suboptimal. Specifically, off-the-shelf CNNs were designed for classification thus never take categories as input features. While in e-commerce, categories are precisely labeled and contain abundant visual priors that will help the visual modeling. Unaware of the ad category, these CNNs may extract some unnecessary category-unrelated features, wasting CNN's limited expression ability. To overcome the two challenges, we propose Category-specific CNN (CSCNN) specially for CTR prediction. CSCNN early incorporates the category knowledge with a light-weighted attention-module on each convolutional layer. This enables CSCNN to extract expressive category-specific visual patterns that benefit the CTR prediction. Offline experiments on benchmark and a 10 billion scale real production dataset from JD, together with an Online A/B test show that CSCNN outperforms all compared state-of-the-art algorithms.