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

Document Type

Conference Proceeding

Publication Date

1-1-2023

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.

Identifier

85185402494 (Scopus)

ISBN

[9798350307887]

Publication Title

Proceedings IEEE International Conference on Data Mining Icdm

External Full Text Location

https://doi.org/10.1109/ICDM58522.2023.00029

ISSN

15504786

First Page

200

Last Page

209

Grant

CNS 2237328

Fund Ref

National Science Foundation

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