Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· 0 citations· 40 references
TL;DR
The key insight of C3Bid is to leverage channel-wise estimation bias as an explicit coupling mechanism, where campaign-level and channel-level bids are combined through bias-aware weighting to produce the final bid.
Abstract
Automated bidding is a core component of modern online advertising systems. With the rapid proliferation of heterogeneous media channels, bidding strategies are required to handle ad requests across multiple channels in a unified manner to maximize total conversions while satisfying shared budget and cost-per-acquisition (CPA) constraints. Unlike existing approaches that decompose budget and CPA constraints into individual channels, we propose Campaign–Channel Coordinated Bidding (C3Bid), a bias-aware automated bidding framework that jointly optimizes final bidding decisions by coordinating campaign and channels under a unified campaign-level objective and constraints. The key insight of C3Bid is to leverage channel-wise estimation bias as an explicit coupling mechanism, where campaign-level and channel-level bids are combined through bias-aware weighting to produce the final bid. We theoretically show that the unified formulation admits a larger feasible solution space and achieves optimal expected conversions under shared constraints, while the proposed bias-aware coordination mechanism further improves channel scalability and robustness to cold-start scenarios. Extensive offline experiments and large-scale online A/B tests in a real-world advertising system demonstrate consistent and significant gains in conversions, budget utilization, and bidding stability under CPA constraints.
This work investigates the design of two-stage auctions that allow advertisers to submit and update their bids, with second-stage bids serving as refinements of the initial ones, and proposes a dynamic two-stage auction that adopts realization-dependent entry fees with discounts.
Yidan Xing, Rui Guo, Yi-Xin Tao et al.· Proceedings of the 32nd ACM...· 0 citations
This work proposes a Causal auto-Bidding method based on a Diffusion completer-aligner framework, termed CBD, which achieves superior performance on large-scale auto-bidding benchmarks, but also delivers significant improvements on an online advertising platform, including a 2.0% increase in target cost.
Ye-Wen Li, Jingtong Gao, Peng Jiang et al.· Proceedings of the 32nd ACM...· 0 citations
Auto-bidding is now widely adopted in online advertising platforms, allowing advertisers to specify high-level campaign objectives--such as maximizing total value subject to a return-on-spend (ROS) constraint--rather than manual per-query bids. A central question in algorithmic mechanism design is characterizing the wo...
Yang Cai, Vineet Gupta, Yan-Chen Jiang et al.· 1 citation
This work extends the familiar fixed-price eCPM to auction dynamics, allowing unified ranking of fixed-price, auction, and ABIN listings under a single objective, and describes a practical production implementation that approximates this objective, addressing cold-start challenges by bootstrapping from existing engagem...
Greg Kocher, Sanjana Arun· 0 citations
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