arXiv Machine Learning

PlatformBid: An Auto-Bidding Benchmark from a Unified Advertising Platform's Perspective

arXiv:2607. 27265v1 Announce Type: new Abstract: Real-time bidding is central to computational advertising, comprising three elements: Supply Side Platform (SSP) selling ad impressions, Demand Side Platform (DSP) bidding for advertisers, and Ad Exchange conducting auctions between them.

arXiv Machine Learning
Jul 22

JD-BP: A Joint-Decision Generative Framework for Auto-Bidding and Pricing

arXiv:2604. 05845v2 Announce Type: replace-cross Abstract: Auto-bidding services optimize real-time bidding strategies for advertisers under key performance indicator (KPI) constraints such as target return on investment and budget.

By Linghui Meng, Chun Gan, Shengsheng Niu, Chengcheng Zhang, Chenchen Li, Chuan Yang, Yi Mao, Xin Zhu, Jie He, Zhangang Lin, Ching Law
arXiv Machine Learning
Jun 15

DRIVE: Distributional and Retrieval-Augmented Bidding with Value Evaluation

arXiv:2606. 14192v1 Announce Type: new Abstract: Auto-bidding is a core component of real-time advertising systems, where decisions must optimize long-term performance under budget and cost constraints, while online exploration is prohibitively risky.

By Miduo Cui, Haochen Wang, Shangqin Mao, Xun Yang, Qianlong Xie, Xingxing Wang, Xuri Ge, Ying Zhou, Zhiwei Xu
arXiv AI
Jun 10

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

arXiv:2606. 09896v1 Announce Type: cross Abstract: In modern online advertising platforms, Guaranteed Delivery (GD) contracts coexist and bid with Real-Time Bidding (RTB) auctions.

By Tianxing Bu, Zhaoqi Zhang, Linyou Cai, Miao Xie, Shengri Xue, Tan Qu, Qianlong Xie, Xingxing Wang, Siqiang Luo, Gao Cong
arXiv AI
Aug 18

Q-Regularized Generative Auto-Bidding: From Suboptimal Trajectories to Optimal Policies

arXiv:2601. 02754v3 Announce Type: replace-cross Abstract: With the rapid development of e-commerce, auto-bidding has become a key asset in optimizing advertising performance under diverse advertiser environments.

By Mingming Zhang, Na Li, Zhuang Feiqing, Hongyang Zheng, Jiangbing Zhou, Wang Wuyin, Sheng-jie Sun, XiaoWei Chen, Junxiong Zhu, Lixin Zou, Chenliang Li
arXiv Machine Learning
Sep 3

Marginal Expected Revenue for Jointly Ranking Auction and Fixed-Price Listings in E-Commerce Sponsored Search

The paper extends the Expected Cost-per-Mille (eCPM) framework to handle auction and hybrid "Auction with Buy It Now" (ABIN) listings by deriving a marginal eCPM (meCPM) that captures the incremental value of showing an additional impression for items whose prices evolve dynamically. This unified ranking objective allows fixed-price, auction, and ABIN listings to be compared and ranked together. A production implementation was tested via online A/B experiments on a large e-commerce platform, yielding positive revenue gains and statistically significant improvements in user metrics, leading to deployment in production.

By Greg Kocher, Sanjana Arun