auto-bidding | BestHub

Alimama Tech

Dec 17, 2024 · Artificial Intelligence

AuctionNet: A Novel Benchmark for Decision-Making in Large-Scale Games

AuctionNet is a newly introduced benchmark that recreates a massive, realistic online advertising auction environment using latent diffusion‑generated traffic data, provides an 80 GB dataset of 5 × 10⁸ logs from 48 bidding agents, and offers baseline evaluations—including an Online LP that outperforms others—supporting thousands of fair NeurIPS 2024 competition submissions and open‑source tools for large‑scale game decision‑making research.

Benchmarkauto-biddinggenerative models

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AuctionNet: A Novel Benchmark for Decision-Making in Large-Scale Games

Kuaishou Tech

Dec 17, 2024 · Artificial Intelligence

NeurIPS 2024 Auto‑Bidding in Large‑Scale Auctions: Kuaishou Team Wins Both General and AIGB Tracks

The NeurIPS 2024 Auto‑Bidding competition attracted over 15,000 submissions and 1,500 teams, featuring two tracks—General and AI‑Generated Bidding—where Kuaishou’s commercial algorithm team secured first place in both by leveraging reinforcement‑learning‑based online exploration and a decision‑transformer‑driven generative approach, achieving more than a 5% lift in ad revenue.

Generative ModelsKuaishouNeurIPS

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NeurIPS 2024 Auto‑Bidding in Large‑Scale Auctions: Kuaishou Team Wins Both General and AIGB Tracks

Alimama Tech

Jul 29, 2024 · Artificial Intelligence

Generative Auto-bidding via Diffusion Modeling (AIGB)

The paper presents AIGB, a generative auto‑bidding framework that replaces reinforcement‑learning with a conditional diffusion model to generate optimal bidding trajectories, and demonstrates through offline benchmarks and Alibaba’s online A/B tests that it consistently outperforms RL baselines, boosting buy count, GMV, and ROI while maintaining low latency.

auto-biddingdiffusion modelinggenerative models

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Generative Auto-bidding via Diffusion Modeling (AIGB)

Alimama Tech

Nov 28, 2023 · Artificial Intelligence

Evolution of Alibaba's AI-Driven Advertising Decision Technologies

The article traces Alibaba’s Alimama platform from classic control‑based bidding through linear programming and reinforcement‑learning approaches to generative‑AI‑driven strategies, detailing how deep‑learning models, offline and sustainable online RL frameworks, and large‑language‑model‑based bidding reshape automated auctions, fairness, and scalability in e‑commerce advertising.

AIadvertisingauction design

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Evolution of Alibaba's AI-Driven Advertising Decision Technologies

Alimama Tech

Apr 12, 2023 · Artificial Intelligence

Truthful Auction Mechanisms for Mixed Utility and Value Maximizers in Online Advertising

The paper introduces two truthful auction mechanisms—MPU for public‑type and MPR for private‑type bidders—that combine VCG payments for utility‑maximizers with GSP payments for value‑maximizers, achieving incentive compatibility, individual rationality, robustness, and a social‑welfare approximation of up to 2 (optimal within a 1.25 factor) in mixed online advertising markets.

GSPVCGauction theory

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Truthful Auction Mechanisms for Mixed Utility and Value Maximizers in Online Advertising

Alimama Tech

Dec 28, 2022 · Artificial Intelligence

Sustainable Online Reinforcement Learning for Auto-bidding (SORL)

The Sustainable Online Reinforcement Learning (SORL) framework tackles offline inconsistency in auto‑bidding by iteratively gathering safe online data from real ad systems with a Lipschitz‑based exploration method and training a variance‑suppressed conservative Q‑learning policy, achieving safer, more stable, and higher‑performing bids on Alibaba’s platform.

Variance Reductionauto-biddingoffline inconsistency

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Sustainable Online Reinforcement Learning for Auto-bidding (SORL)

Alimama Tech

Mar 9, 2022 · Artificial Intelligence

Multi-Agent Auto-bidding (MAAB): A Framework for Distributed Automatic Bidding in Online Advertising

The paper introduces MAAB, a scalable multi‑agent reinforcement‑learning framework for online ad bidding that uses temperature‑regularized credit assignment, adaptive threshold agents, and mean‑field clustering to balance individual advertiser utility, platform revenue, and overall social welfare in competitive auction environments.

Multi-Agent Reinforcement Learningauto-biddingmean field

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Multi-Agent Auto-bidding (MAAB): A Framework for Distributed Automatic Bidding in Online Advertising