mlsanityData to 7 Oct 2026

Bidding Agent Design in the LinkedIn Ad Marketplace

arXiv 2202.12472 ↗First posted 25 Feb 2022cs.GT cs.AI cs.IRcited by 7

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Abstract (arXiv)

We establish a general optimization framework for the design of automated bidding agent in dynamic online marketplaces. It optimizes solely for the buyer's interest and is agnostic to the auction mechanism imposed by the seller. As a result, the framework allows, for instance, the joint optimization of a group of ads across multiple platforms each running its own auction format. Bidding strategy derived from this framework automatically guarantees the optimality of budget allocation across ad units and platforms. Common constraints such as budget delivery schedule, return on investments and guaranteed results, directly translates to additional parameters in the bidding formula. We share practical learnings of the deployed bidding system in the LinkedIn ad marketplace based on this framework.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 7 of its 27 references that are in the map. First posted on arXiv 25 Feb 2022.

Builds on

7 of 27 references.

References · 7

  1. 2019Introduction to Online Convex Optimizationcited by 793
  2. 2020The Best of Many Worlds: Dual Mirror Descent for Online Allocation Problemscited by 58
  3. 2021Stochastic Bandits for Multi-platform Budget Optimization in Online Advertisingcited by 25
  4. 2016Feedback Control of Real-Time Display Advertisingcited by 22
  5. 2015Smart Pacing for Effective Online Ad Campaign Optimizationcited by 14
  6. 2017Profit Maximization for Online Advertising Demand-Side Platformscited by 9
  7. 2015Optimal Real-Time Bidding Strategiescited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 1; 2025: 1; 2026: 1.

7 papers.