# Adaptive Risk-Aware Bidding with Budget Constraint in Display Advertising

arXiv: https://arxiv.org/abs/2212.12533 · HTML: https://mlsanity.com/paper/2212.12533/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 45 references that are in the map.
- First posted on arXiv 6 Dec 2022.

## Builds on (most cited first, 14 of 14)

- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Neural Factorization Machines for Sparse Predictive Analytics](https://mlsanity.com/paper/1708.05027/) (2017)
- [Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](https://mlsanity.com/paper/1708.04617/) (2017)
- [Real-Time Bidding by Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1701.02490/) (2017)
- [Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction](https://mlsanity.com/paper/1601.02376/) (2016)
- [Deep Session Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1905.06482/) (2019)
- [Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1802.08365/) (2018)
- [Experience Replay Optimization](https://mlsanity.com/paper/1906.08387/) (2019)
- [Real-time Bidding for Online Advertising: Measurement and Analysis](https://mlsanity.com/paper/1306.6542/) (2013)
- [Statistical Arbitrage Mining for Display Advertising](https://mlsanity.com/paper/1506.03837/) (2015)
- [Recurrent Neural Networks for Stochastic Control in Real-Time Bidding](https://mlsanity.com/paper/2006.07042/) (2020)
- [Managing Risk of Bidding in Display Advertising](https://mlsanity.com/paper/1701.02433/) (2017)

## Built on by (most cited first, 1 of 1)

- [Hierarchical Multi-agent Meta-Reinforcement Learning for Cross-channel Bidding](https://mlsanity.com/paper/2412.19064/) (2024)

## Abstract (arXiv)

Real-time bidding (RTB) has become a major paradigm of display advertising. Each ad impression generated from a user visit is auctioned in real time, where demand-side platform (DSP) automatically provides bid price usually relying on the ad impression value estimation and the optimal bid price determination. However, the current bid strategy overlooks large randomness of the user behaviors (e.g., click) and the cost uncertainty caused by the auction competition. In this work, we explicitly factor in the uncertainty of estimated ad impression values and model the risk preference of a DSP under a specific state and market environment via a sequential decision process. Specifically, we propose a novel adaptive risk-aware bidding algorithm with budget constraint via reinforcement learning, which is the first to simultaneously consider estimation uncertainty and the dynamic risk tendency of a DSP. We theoretically unveil the intrinsic relation between the uncertainty and the risk tendency based on value at risk (VaR). Consequently, we propose two instantiations to model risk tendency, including an expert knowledge-based formulation embracing three essential properties and an adaptive learning method based on self-supervised reinforcement learning. We conduct extensive experiments on public datasets and show that the proposed framework outperforms state-of-the-art methods in practical settings.