mlsanityData to 7 Oct 2026

Risk-Aware Bid Optimization for Online Display Advertisement

arXiv 2210.15837 ↗First posted 28 Oct 2022cs.LG cs.GT cs.IRcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 48 references that are in the map. First posted on arXiv 28 Oct 2022.

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20 of 48 references.

References · 20

  1. 2016Wide & Deep Learning for Recommender Systemscited by 745
  2. 2017DeepFM: A Factorization-Machine based Neural Network for CTR Predictioncited by 673
  3. 2017Real-Time Bidding by Reinforcement Learning in Display Advertisingcited by 90
  4. 2019FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Predictioncited by 83
  5. 2018Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertisingcited by 65
  6. 2018Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertisingcited by 60
  7. 2018Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertisingcited by 59
  8. 2018Product-based Neural Networks for User Response Prediction over Multi-field Categorical Datacited by 44
  9. 2019Bid Optimization by Multivariable Control in Display Advertisingcited by 37
  10. 2018Deep Reinforcement Learning for Sponsored Search Real-time Biddingcited by 34
  11. 2017Field-aware Factorization Machines in a Real-world Online Advertising Systemcited by 21
  12. 2018Bidding Machine: Learning to Bid for Directly Optimizing Profits in Display Advertisingcited by 21
8 more
  1. 2014Real-Time Bidding Benchmarking with iPinYou Datasetcited by 19
  2. 2019Deep Landscape Forecasting for Real-time Bidding Advertisingcited by 12
  3. 2015Optimal Real-Time Bidding Strategiescited by 8
  4. 2016Cost-sensitive Learning for Utility Optimization in Online Advertising Auctionscited by 7
  5. 2020Recurrent Neural Networks for Stochastic Control in Real-Time Biddingcited by 6
  6. 2017Managing Risk of Bidding in Display Advertisingcited by 5
  7. 2020Field-Embedded Factorization Machines for Click-through rate predictioncited by 4
  8. 2017Dual Based DSP Bidding Strategy and its Applicationcited by 1

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