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Bandits for Sponsored Search Auctions under Unknown Valuation Model: Case Study in E-Commerce Advertising

arXiv 2304.00999 ↗First posted 31 Mar 2023cs.GT cs.AI cs.LGcited by 0

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Abstract (arXiv)

This paper presents a bidding system for sponsored search auctions under an unknown valuation model. This formulation assumes that the bidder's value is unknown, evolving arbitrarily, and observed only upon winning an auction. Unlike previous studies, we do not impose any assumptions on the nature of feedback and consider the problem of bidding in sponsored search auctions in its full generality. Our system is based on a bandit framework that is resilient to the black-box auction structure and delayed and batched feedback. To validate our proposed solution, we conducted a case study at Zalando, a leading fashion e-commerce company. We outline the development process and describe the promising outcomes of our bandits-based approach to increase profitability in sponsored search auctions. We discuss in detail the technical challenges that were overcome during the implementation, shedding light on the mechanisms that led to increased profitability.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 36 references that are in the map. First posted on arXiv 31 Mar 2023.

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21 of 36 references.

References · 21

  1. 2018Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Banditscited by 124
  2. 2015Explore no more: Improved high-probability regret bounds for non-stochastic banditscited by 120
  3. 2017Real-Time Bidding by Reinforcement Learning in Display Advertisingcited by 91
  4. 2018What Doubling Tricks Can and Can't Do for Multi-Armed Banditscited by 71
  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 61
  7. 2017Stochastic Bandit Models for Delayed Conversionscited by 54
  8. 2015Online learning in repeated auctionscited by 39
  9. 2017Learning to Bid Without Knowing your Valuecited by 34
  10. 2020Optimal No-regret Learning in Repeated First-price Auctionscited by 33
  11. 2020Learning to Bid Optimally and Efficiently in Adversarial First-price Auctionscited by 33
  12. 2021Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertisingcited by 31
9 more
  1. 2021Stochastic Bandits for Multi-platform Budget Optimization in Online Advertisingcited by 25
  2. 2021A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertisingcited by 22
  3. 2020Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertisingcited by 13
  4. 2022Leveraging the Hints: Adaptive Bidding in Repeated First-Price Auctionscited by 11
  5. 2022Bidding Agent Design in the LinkedIn Ad Marketplacecited by 7
  6. 2014Learning Valuation Distributions from Partial Observationcited by 3
  7. 2022Analysis of Dual-Based PID Controllers through Convolutional Mirror Descentcited by 3
  8. 2022The Impact of Batch Learning in Stochastic Linear Banditscited by 3
  9. 2013Prior-independent Auctions for Risk-averse Agentscited by 1

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