← 研踪 PaperTrailarXiv:2508.08772 · v3
Certified Boost Design for Quality-Aware Auto-Bidding
首次提交 2025-08-12 17:20
版本更新 2026-11-08 01:00
雷达观测 2026-11-10 11:14
摘要
Online advertising platforms balance advertiser value, publisher-assessed ad quality, and platform revenue. However, boost design in this three-stakeholder architecture remains insufficiently studied. In this paper, we develop certified boost mechanisms under a payment rule that deducts boosts from winners' payments. We establish a capped benchmark-competitive condition that guarantees a quality-aware liquid-welfare lower bound parameterized by the certificate strength, boost cap, and bid cap, with an explicit penalty for an insufficient boost cap. We then derive a closed-form canonical boost. For the observed state, it matches the welfare-optimal benchmark allocation whenever its certificate margin seats every benchmark winner, and among all certified boosts that do so it maximizes the platform's retained revenue. On AuctionNet, a public benchmark built on Alibaba's real-world production advertising environment, we evaluate canonical across ten bidding policies on multiple episodes. Under a twofold shock to the strongest rival's bid, canonical achieves $97.6\%$ mean welfare efficiency, compared with $91.1\%$ for the strongest baselines. Controlled stress tests link this protection to the certificate margin, while a ten-policy cap sweep shows diminishing welfare gains as retained revenue falls.
作者备注：13 pages
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v3 · 2026-11-08 01:00 · 最新观测版本
Certified Boost Design for Quality-Aware Auto-Bidding
Online advertising platforms balance advertiser value, publisher-assessed ad quality, and platform revenue. However, boost design in this three-stakeholder architecture remains insufficiently studied. In this paper, we develop certified boost mechanisms under a payment rule that deducts boosts from winners' payments. We establish a capped benchmark-competitive condition that guarantees a quality-aware liquid-welfare lower bound parameterized by the certificate strength, boost cap, and bid cap, with an explicit penalty for an insufficient boost cap. We then derive a closed-form canonical boost. For the observed state, it matches the welfare-optimal benchmark allocation whenever its certificate margin seats every benchmark winner, and among all certified boosts that do so it maximizes the platform's retained revenue. On AuctionNet, a public benchmark built on Alibaba's real-world production advertising environment, we evaluate canonical across ten bidding policies on multiple episodes. Under a twofold shock to the strongest rival's bid, canonical achieves $97.6\%$ mean welfare efficiency, compared with $91.1\%$ for the strongest baselines. Controlled stress tests link this protection to the certificate margin, while a ten-policy cap sweep shows diminishing welfare gains as retained revenue falls.
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