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A Personalized Automated Bidding Framework for Fairness-aware Online Advertising

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Bo ZhengAuthors Info & Claims

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 5544 - 5553

https://doi.org/10.1145/3580305.3599765

Published: 04 August 2023 Publication History

Abstract

Powered by machine learning techniques, online advertising platforms have launched various automated bidding strategy services to facilitate intelligent decision-making for advertisers. However, advertisers experience heterogeneous advertising environments, and thus the unified bidding strategies widely used in both academia and industry suffer from severe unfairness issues, resulting in significant ad performance disparity among advertisers. In this work, to resolve the unfairness issue and improve the overall system performance, we propose a personalized automated bidding framework, namely PerBid, shifting the classical automated bidding strategy with a unified agent to multiple context-aware agents corresponding to different advertiser clusters. Specifically, we first design an ad campaign profiling network to model dynamic advertising environments. By clustering the advertisers with similar profiles and generating context-aware automated bidding agents for each cluster, we can match advertisers with personalized automated bidding strategies. Experiments conducted on the real-world dataset and online A/B test on Alibaba display advertising platform demonstrate the effectiveness of PerBid in improving overall ad performance and guaranteeing fairness among heterogeneous advertisers.

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Cited By

Su YXiang MChen YLi YQin TZhang HLi YLiu XBaeza-Yates RBonchi F(2024)Spending Programmed Bidding: Privacy-friendly Bid Optimization with ROI Constraint in Online AdvertisingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671540(5731-5740)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671540
Guo JHuo YZhang ZWang TYu CXu JZheng BZhang YBaeza-Yates RBonchi F(2024)Generative Auto-bidding via Conditional Diffusion ModelingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671526(5038-5049)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671526

Index Terms

A Personalized Automated Bidding Framework for Fairness-aware Online Advertising
1. Applied computing
  1. Electronic commerce
2. Information systems
  1. World Wide Web
    1. Online advertising
      1. Display advertising
    2. Web applications
      1. Electronic commerce

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Published In

cover image ACM Conferences

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 2023

5996 pages

ISBN:9798400701030

DOI:10.1145/3580305

General Chairs:
Ambuj Singh
UC Santa Barbara, USA
,
Yizhou Sun
UC Los Angeles, USA
,
Program Chairs:
Leman Akoglu
Carnegie Mellon University, USA
,
Dimitrios Gunopulos
University of Athens, Greece
,
Xifeng Yan
UC Santa Barbara, USA
,
Ravi Kumar
Google, USA
,
Fatma Ozcan
Google, USA
,
Jieping Ye
Alibaba DAMO Academy

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 04 August 2023

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China NSF grant
Shanghai Science and Technology fund
National Key R&D Program of China

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KDD '23

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KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 6 - 10, 2023

CA, Long Beach, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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August 3 - 7, 2025

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Cited By

Su YXiang MChen YLi YQin TZhang HLi YLiu XBaeza-Yates RBonchi F(2024)Spending Programmed Bidding: Privacy-friendly Bid Optimization with ROI Constraint in Online AdvertisingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671540(5731-5740)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671540
Guo JHuo YZhang ZWang TYu CXu JZheng BZhang YBaeza-Yates RBonchi F(2024)Generative Auto-bidding via Conditional Diffusion ModelingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671526(5038-5049)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671526

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