# A Hybrid Bandit Model with Visual Priors for Creative Ranking in Display Advertising

arXiv: https://arxiv.org/abs/2102.04033 · HTML: https://mlsanity.com/paper/2102.04033/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 36 references that are in the map.
- First posted on arXiv 8 Feb 2021.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [NIMA: Neural Image Assessment](https://mlsanity.com/paper/1709.05424/) (2017)
- [Learning to Optimize Via Posterior Sampling](https://mlsanity.com/paper/1301.2609/) (2013)
- [An Introduction to Deep Reinforcement Learning](https://mlsanity.com/paper/1811.12560/) (2018)
- [Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling](https://mlsanity.com/paper/1802.09127/) (2018)
- [Photo Aesthetics Ranking Network with Attributes and Content Adaptation](https://mlsanity.com/paper/1606.01621/) (2016)
- [Deep CTR Prediction in Display Advertising](https://mlsanity.com/paper/1609.06018/) (2016)
- [Aesthetic-based Clothing Recommendation](https://mlsanity.com/paper/1809.05822/) (2018)
- [Will People Like Your Image? Learning the Aesthetic Space](https://mlsanity.com/paper/1611.05203/) (2016)
- [Image Matters: Visually modeling user behaviors using Advanced Model Server](https://mlsanity.com/paper/1711.06505/) (2017)
- [Category-Specific CNN for Visual-aware CTR Prediction at JD.com](https://mlsanity.com/paper/2006.10337/) (2020)
- [Hierarchical Adaptive Contextual Bandits for Resource Constraint based Recommendation](https://mlsanity.com/paper/2004.01136/) (2020)
- [A Computational Approach to Relative Aesthetics](https://mlsanity.com/paper/1704.01248/) (2017)
- [Telepath: Understanding Users from a Human Vision Perspective in Large-Scale Recommender Systems](https://mlsanity.com/paper/1709.00300/) (2017)

## Built on by (most cited first, 15 of 15)

- [OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text Generation](https://mlsanity.com/paper/2411.18499/) (2024)
- [A New Creative Generation Pipeline for Click-Through Rate with Stable Diffusion Model](https://mlsanity.com/paper/2401.10934/) (2024)
- [Towards Reliable Advertising Image Generation Using Human Feedback](https://mlsanity.com/paper/2408.00418/) (2024)
- [CTR-Driven Advertising Image Generation with Multimodal Large Language Models](https://mlsanity.com/paper/2502.06823/) (2025)
- [Boost CTR Prediction for New Advertisements via Modeling Visual Content](https://mlsanity.com/paper/2209.11727/) (2022)
- [Parallel Ranking of Ads and Creatives in Real-Time Advertising Systems](https://mlsanity.com/paper/2312.12750/) (2023)
- [T-Stars-Poster: A Framework for Product-Centric Advertising Image Design](https://mlsanity.com/paper/2501.14316/) (2025)
- [Diffusion Models in Recommendation Systems: A Survey](https://mlsanity.com/paper/2501.10548/) (2025)
- [No-Regret Linear Bandits beyond Realizability](https://mlsanity.com/paper/2302.13252/) (2023)
- [Creative4U: MLLMs-based Advertising Creative Image Selector with Comparative Reasoning](https://mlsanity.com/paper/2508.12628/) (2025)
- [Convolutional Neural Bandit for Visual-aware Recommendation](https://mlsanity.com/paper/2107.07438/) (2021)
- [AdSEE: Investigating the Impact of Image Style Editing on Advertisement Attractiveness](https://mlsanity.com/paper/2309.08159/) (2023)
- [PinDCO: Whole-Page Aware Dynamic Creative Optimization at Scale](https://mlsanity.com/paper/2609.11943/) (2026)
- [KwaiMind Technical Report](https://mlsanity.com/paper/2609.26375/) (2026)
- [Evolving Creatives with Clicks: CTR-Driven Advertising image Editing via Attribute Disentanglement and Preference Alignment](https://mlsanity.com/paper/2610.13066/) (2026)

## Abstract (arXiv)

Creative plays a great important role in e-commerce for exhibiting products. Sellers usually create multiple creatives for comprehensive demonstrations, thus it is crucial to display the most appealing design to maximize the Click-Through Rate~(CTR). For this purpose, modern recommender systems dynamically rank creatives when a product is proposed for a user. However, this task suffers more cold-start problem than conventional products recommendation In this paper, we propose a hybrid bandit model with visual priors which first makes predictions with a visual evaluation, and then naturally evolves to focus on the specialities through the hybrid bandit model. Our contributions are three-fold: 1) We present a visual-aware ranking model (called VAM) that incorporates a list-wise ranking loss for ordering the creatives according to the visual appearance. 2) Regarding visual evaluations as a prior, the hybrid bandit model (called HBM) is proposed to evolve consistently to make better posteriori estimations by taking more observations into consideration for online scenarios. 3) A first large-scale creative dataset, CreativeRanking, is constructed, which contains over 1.7M creatives of 500k products as well as their real impression and click data. Extensive experiments have also been conducted on both our dataset and public Mushroom dataset, demonstrating the effectiveness of the proposed method.