mlsanityData to 14 Oct 2026

HMAF: A Hierarchical Multi-Slot GD-RTB Allocation Framework

arXiv 2606.09896 ↗First posted 4 Jun 2026cs.GT cs.AI cs.LGcited by 0

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Abstract (arXiv)

In modern online advertising platforms, Guaranteed Delivery (GD) contracts coexist and bid with Real-Time Bidding (RTB) auctions. Recent approaches either decouple GD and RTB optimization or rely on heuristic priority rules, and thus fail to effectively balance short-term revenue maximization with long-term contract delivery under complex multi-slot delivery and impression constraints. To address these challenges, we propose HMAF (Hierarchical Multi-Slot Allocation Framework), a unified framework designed to optimize impression allocation in GD--RTB advertising platforms. HMAF employs the Plan--Calibrate--Execute paradigm as its core structure, and integrates offline constraint optimization with online decision-making, balancing offline GD resource planning, dynamically calibrating GD--RTB competitiveness, and making real-time listwise rank decisions across multi-slot environments. HMAF has been implemented in multiple marketing scenarios at Meituan, one of the world's largest online food delivery platforms, leading to a 3.72% increase in GD delivery rate and a 1.59% increase in total advertisement revenue.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 27 references that are in the map. First posted on arXiv 4 Jun 2026.

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References · 5

  1. 2013Real-time Bidding for Online Advertising: Measurement and Analysiscited by 32
  2. 2016Feedback Control of Real-Time Display Advertisingcited by 22
  3. 2023RLTP: Reinforcement Learning to Pace for Delayed Impression Modeling in Preloaded Adscited by 4
  4. 2022Impression Allocation and Policy Search in Display Advertisingcited by 2
  5. 2026Beyond Single Slot: Joint Optimization for Multi-Slot Guaranteed Display Advertisingcited by 1

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