Maximizing revenue for publishers using header bidding and ad exchange auctions

Jason Rhuggenaath (Corresponding author), Reza Refaei Afshar, Alp Akcay, Yingqian Zhang, Uzay Kaymak, Fatih Çolak, Muratcan Tanyerli

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)


We study how web publishers should set their floor prices in order to maximize expected revenues when they have access to two selling mechanisms, namely an ad exchange and header bidding, in order to sell impressions on the real-time bidding market. We consider the publisher’s problem under incomplete information, propose bandit-type algorithms, and show that their regret – the performance loss compared to the optimal algorithm – is sub-linear in the time horizon. Experiments illustrate the effectiveness of our algorithms.
Original languageEnglish
Pages (from-to)250-256
Number of pages7
JournalOperations Research Letters
Issue number2
Publication statusPublished - Mar 2021


  • Auctions
  • Floor price
  • Header bidding
  • Machine learning
  • Multi-armed bandits
  • Pricing


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