tutorial

A Survey on Bid Optimization in Real-Time Bidding Display Advertising

Authors:

Yong YuAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 18, Issue 3

Article No.: 58, Pages 1 - 31

https://doi.org/10.1145/3628603

Published: 09 December 2023 Publication History

Abstract

Real-Time Bidding (RTB) is one of the most important forms of online advertising, where an auction is hosted in real time to sell the individual ad impression. How to design an automated bidding strategy in response to the dynamic auction environment is crucial for improving user experience, protecting the interests of advertisers, and promoting the long-term development of the advertising platform. As an exciting topic in the real-world industry, it has attracted great research interest from several disciplines, most notably data science. There have been abundant studies on bidding strategy design which are based on the large volume of historical ad requests. Despite its popularity and significance, few works provide a summary for bid optimization. In this survey, we present the latest overview of the recent works to shed light on the optimization techniques where most of them are validated in practice. We first explore the optimization problem in different works, explaining how these different settings affect the bidding strategy designs. Then, some forms of bidding functions and specific optimization techniques are illustrated. Further, we specifically discuss a new trend about bidding in first-price auctions, which have gradually become popular in recent years. From this survey, both practitioners and researchers can gain insights of the challenges and future prospects of bid optimization in RTB.

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Cited By

Gigli MStella F(2024)Multi-armed bandits for performance marketingInternational Journal of Data Science and Analytics10.1007/s41060-023-00493-7Online publication date: 19-Jan-2024
https://doi.org/10.1007/s41060-023-00493-7
Provodin DJoudioux JDuryev E(2024)Bandits for Sponsored Search Auctions Under Unknown Valuation Model: Case Study in E-Commerce AdvertisingMachine Learning and Knowledge Discovery in Databases. Applied Data Science Track10.1007/978-3-031-70381-2_17(263-279)Online publication date: 22-Aug-2024
https://doi.org/10.1007/978-3-031-70381-2_17

Index Terms

A Survey on Bid Optimization in Real-Time Bidding Display Advertising
1. Applied computing
  1. Electronic commerce
    1. Online auctions
  2. Operations research
    1. Marketing

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 18, Issue 3

April 2024

663 pages

EISSN:1556-472X

DOI:10.1145/3613567

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 December 2023

Online AM: 18 October 2023

Accepted: 12 October 2023

Revised: 12 August 2023

Received: 19 January 2023

Published in TKDD Volume 18, Issue 3

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Gigli MStella F(2024)Multi-armed bandits for performance marketingInternational Journal of Data Science and Analytics10.1007/s41060-023-00493-7Online publication date: 19-Jan-2024
https://doi.org/10.1007/s41060-023-00493-7
Provodin DJoudioux JDuryev E(2024)Bandits for Sponsored Search Auctions Under Unknown Valuation Model: Case Study in E-Commerce AdvertisingMachine Learning and Knowledge Discovery in Databases. Applied Data Science Track10.1007/978-3-031-70381-2_17(263-279)Online publication date: 22-Aug-2024
https://doi.org/10.1007/978-3-031-70381-2_17

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