Reserve Price optimization in First-Price Auctions via Multi-Task Learning

Achir Kalra, Chong Wang, Cristian Borcea, Yi Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Online publishers typically sell ad impressions through auctions held in ad exchanges in real-time, i.e., real-time bidding (RTB). A publisher will accept the winning bid if it is higher than a given reserve price for an ad impression. Setting an appropriate reserve price for an ad impression is critical for publishers' revenue generation, but also challenging. While this problem has been studied for second-price auctions, it lacks studies for first-price auctions, the de facto industry standard since 2019. This paper proposes a machine learning model that determines the optimal reserve prices for individual ad impressions in real-time. It uses a multi-task learning framework to predict the lower bounds of the highest bids with a coverage probability, using only the data available to publishers. The experiments using data from a large international publisher show that the proposed model outperforms the comparison systems on generating revenue.

Original languageEnglish (US)
Title of host publicationProceedings - 23rd IEEE International Conference on Data Mining, ICDM 2023
EditorsGuihai Chen, Latifur Khan, Xiaofeng Gao, Meikang Qiu, Witold Pedrycz, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages200-209
Number of pages10
ISBN (Electronic)9798350307887
DOIs
StatePublished - 2023
Event23rd IEEE International Conference on Data Mining, ICDM 2023 - Shanghai, China
Duration: Dec 1 2023Dec 4 2023

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Conference

Conference23rd IEEE International Conference on Data Mining, ICDM 2023
Country/TerritoryChina
CityShanghai
Period12/1/2312/4/23

All Science Journal Classification (ASJC) codes

  • General Engineering

Keywords

  • computational advertising
  • neural networks
  • prediction interval estimation
  • proportional hazards model
  • survival analysis

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