Abstract:Guaranteed display advertising is crucial for platform monetization, yet existing methods often operate under a single-slot assumption, limiting their ability to optimize allocation across multi-slot page views. In this paper, we propose a novel joint optimization framework for multi-slot GD allocation, addressing key challenges such as slot-level redundancy, contract imbalance, and exposure concentration. Our approach formulates the allocation as an offline bipartite matching problem with a contract roulette mechanism for slot exclusivity and Page View constraints for impression control, and incorporates a scalable allocation optimization algorithm for efficient large-scale deployment. Extensive online tests on the Meituan advertising platform demonstrate that our method significantly improves merchant ROI, platform revenue efficiency, and contract fulfillment robustness. Specifically, online A/B tests show a 28.99% increase in Average Revenue Per User under 70% traffic, and DID analysis further indicates improved contract stability, demonstrating the strong applicability and effectiveness of our framework in real-world advertising deployments.
| Comments: | Accepted at SIGIR Industry Track 2026 |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.21556 [cs.LG] |
| (or arXiv:2605.21556v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.21556 arXiv-issued DOI via DataCite |
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| Related DOI: | https://doi.org/10.1145/3805712.3808398
DOI(s) linking to related resources |
From: Zhaoqi Zhang [view email]
[v1]
Wed, 20 May 2026 13:32:47 UTC (2,491 KB)
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