Work

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

Year: 2026

Type: conference-paper

Abstract: 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 on GD delivery rate and 1.59% increase on total advertisement revenue.

Source: ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

DOI: 10.1145/3770855.3818434

Authors: Tianxing Bu, Zhaoqi Zhang, Linyou Cai, Miao Xie, Shengri Xue, Tan Qu, Qianlong Xie, Xingxing Wang, Siqiang Luo, Gao Cong

Institutions: Meituan (China), Nanyang Technological University, China Agricultural University

Topic: Auction Theory and Applications

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: hybrid

OpenAlex ID: W7164398656

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