Truthful Auctions for Automated Bidding in Online Advertising
Yidan Xing, Zhilin Zhang, Zhenzhe Zheng, Chuan Yu, Jian Xu, Fan Wu and, Guihai Chen

TL;DR
This paper introduces a new truthful auction model for automated online advertising bidding that accounts for private financial constraints, proposing a personalized scoring mechanism that improves over traditional auction formats.
Contribution
It develops a novel multi-dimensional auction framework with private constraints and designs a personalized rank score auction that ensures truthfulness and better performance.
Findings
The proposed auction outperforms first-price and second-price auctions in experiments.
A new characterization of truthful multi-dimensional auctions with private constraints.
A practical auction design using personalized rank scores for automated bidding.
Abstract
Automated bidding, an emerging intelligent decision making paradigm powered by machine learning, has become popular in online advertising. Advertisers in automated bidding evaluate the cumulative utilities and have private financial constraints over multiple ad auctions in a long-term period. Based on these distinct features, we consider a new ad auction model for automated bidding: the values of advertisers are public while the financial constraints, such as budget and return on investment (ROI) rate, are private types. We derive the truthfulness conditions with respect to private constraints for this multi-dimensional setting, and demonstrate any feasible allocation rule could be equivalently reduced to a series of non-decreasing functions on budget. However, the resulted allocation mapped from these non-decreasing functions generally follows an irregular shape, making it difficult to…
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Taxonomy
TopicsAuction Theory and Applications · Consumer Market Behavior and Pricing
