Conference proceeding
Multi-Platform Autobidding with and without Predictions
Proceedings of the ACM on Web Conference 2025, pp 2850-2859
28 Apr 2025
Abstract
We study the problem of finding the optimal bidding strategy for an advertiser in a multi-platform auction setting. The competition on a platform is captured by a value and a cost function, mapping bidding strategies to value and cost respectively. We assume a diminishing returns property, whereby the marginal cost is increasing in value. The advertiser uses an autobidder that selects a bidding strategy for each platform, aiming to maximize total value subject to budget and return-on-spend constraint. The advertiser has no prior information and learns about the value and cost functions by querying a platform with a specific bidding strategy. Our goal is to design algorithms that find the optimal bidding strategy with a small number of queries.
We first present an algorithm that requires (O(m log (mn) log n)) queries, where m is the number of platforms and n is the number of possible bidding strategies in each platform. Moreover, we adopt the learning-augmented framework and propose an algorithm that utilizes a (possibly erroneous) prediction of the optimal bidding strategy. We provide a O(m log (mη) log η) query-complexity bound on our algorithm as a function of the prediction error η. This guarantee gracefully degrades to (O(m log (mn) log n)). This achieves a "best-of-both-worlds" scenario: (O(m)) queries when given a correct prediction, and (O(m log (mn) log n)) even for an arbitrary incorrect prediction.
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Details
- Title
- Multi-Platform Autobidding with and without Predictions
- Creators
- Gagan Aggarwal - Google (United States)Anupam Gupta - New York UniversityXizhi Tan - Drexel UniversityMingfei Zhao - Google (United States)
- Publication Details
- Proceedings of the ACM on Web Conference 2025, pp 2850-2859
- Conference
- WWW '25: The ACM Web Conference 2025
- Series
- ACM Conferences
- Publisher
- Association for Computing Machinery
- Number of pages
- 10
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:001505285200236
- Scopus ID
- 2-s2.0-105005140363
- Other Identifier
- 991022062817104721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Industry collaboration
- Domestic collaboration
- Web of Science research areas
- Computer Science, Interdisciplinary Applications