# Risk-Aware Bid Optimization for Online Display Advertisement

arXiv: https://arxiv.org/abs/2210.15837 · HTML: https://mlsanity.com/paper/2210.15837/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 48 references that are in the map.
- First posted on arXiv 28 Oct 2022.

## Builds on (most cited first, 20 of 20)

- [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)
- [Real-Time Bidding by Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1701.02490/) (2017)
- [FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction](https://mlsanity.com/paper/1905.09433/) (2019)
- [Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1802.09756/) (2018)
- [Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1802.08365/) (2018)
- [Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising](https://mlsanity.com/paper/1806.03514/) (2018)
- [Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data](https://mlsanity.com/paper/1807.00311/) (2018)
- [Bid Optimization by Multivariable Control in Display Advertising](https://mlsanity.com/paper/1905.10928/) (2019)
- [Deep Reinforcement Learning for Sponsored Search Real-time Bidding](https://mlsanity.com/paper/1803.00259/) (2018)
- [Field-aware Factorization Machines in a Real-world Online Advertising System](https://mlsanity.com/paper/1701.04099/) (2017)
- [Bidding Machine: Learning to Bid for Directly Optimizing Profits in Display Advertising](https://mlsanity.com/paper/1803.02194/) (2018)
- [Real-Time Bidding Benchmarking with iPinYou Dataset](https://mlsanity.com/paper/1407.7073/) (2014)
- [Deep Landscape Forecasting for Real-time Bidding Advertising](https://mlsanity.com/paper/1905.03028/) (2019)
- [Optimal Real-Time Bidding Strategies](https://mlsanity.com/paper/1511.08409/) (2015)
- [Cost-sensitive Learning for Utility Optimization in Online Advertising Auctions](https://mlsanity.com/paper/1603.03713/) (2016)
- [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)
- [Field-Embedded Factorization Machines for Click-through rate prediction](https://mlsanity.com/paper/2009.09931/) (2020)
- [Dual Based DSP Bidding Strategy and its Application](https://mlsanity.com/paper/1705.09416/) (2017)

## Abstract (arXiv)

This research focuses on the bid optimization problem in the real-time bidding setting for online display advertisements, where an advertiser, or the advertiser's agent, has access to the features of the website visitor and the type of ad slots, to decide the optimal bid prices given a predetermined total advertisement budget. We propose a risk-aware data-driven bid optimization model that maximizes the expected profit for the advertiser by exploiting historical data to design upfront a bidding policy, mapping the type of advertisement opportunity to a bid price, and accounting for the risk of violating the budget constraint during a given period of time. After employing a Lagrangian relaxation, we derive a parametrized closed-form expression for the optimal bidding strategy. Using a real-world dataset, we demonstrate that our risk-averse method can effectively control the risk of overspending the budget while achieving a competitive level of profit compared with the risk-neutral model and a state-of-the-art data-driven risk-aware bidding approach.