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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.21556 |
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| _version_ | 1866917518047707136 |
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| author | Zhang, Zhaoqi Deng, Jiaming Xie, Miao Cai, Linyou Xie, Qianlong Wang, Xingxing Luo, Siqiang Cong, Gao |
| author_facet | Zhang, Zhaoqi Deng, Jiaming Xie, Miao Cai, Linyou Xie, Qianlong Wang, Xingxing Luo, Siqiang Cong, Gao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_21556 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Beyond Single Slot: Joint Optimization for Multi-Slot Guaranteed Display Advertising Zhang, Zhaoqi Deng, Jiaming Xie, Miao Cai, Linyou Xie, Qianlong Wang, Xingxing Luo, Siqiang Cong, Gao Machine Learning 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. |
| title | Beyond Single Slot: Joint Optimization for Multi-Slot Guaranteed Display Advertising |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.21556 |