online advertising

Alimama Tech

Jan 8, 2025 · Artificial Intelligence

Model-Based Reinforcement Learning Auto‑Bidding Algorithms for Online Advertising

The paper introduces a model‑based reinforcement‑learning auto‑bidding framework that learns a neural‑network environment model from real logs, generates confidence‑aware virtual data fused with real data, and employs the COMBO+MICRO stabilizer and a Lagrange‑dual method for ROI‑constrained bidding, delivering up to 6.8 % higher consumption, 5 % GMV growth and 3.7 % ROI improvement on Alibaba’s platform.

auto-biddingbudget constrained biddingmodel-based RL

0 likes · 22 min read

Model-Based Reinforcement Learning Auto‑Bidding Algorithms for Online Advertising

Alimama Tech

Dec 25, 2024 · Artificial Intelligence

Contextual Generative Auction with Permutation-level Externalities for Online Advertising

The paper introduces Contextual Generative Auction (CGA), a generative framework that directly optimizes ad placements while modeling permutation‑level externalities, decouples allocation from payment learning, and achieves near‑optimal Myerson‑style outcomes, delivering up to 3.2% higher RPM, 1.4% more CTR, 6.4% GMV growth, and 3.5% increased advertiser ROI in large‑scale Taobao experiments.

Externalitiesauction theorygenerative models

0 likes · 18 min read

Contextual Generative Auction with Permutation-level Externalities for Online Advertising

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.

auto-biddingbenchmarkgenerative models

0 likes · 15 min read

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

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

0 likes · 18 min read

Generative Auto-bidding via Diffusion Modeling (AIGB)

Alimama Tech

Jul 11, 2024 · Artificial Intelligence

Efficient Local Search for Guaranteed Display Advertising Inventory Allocation with Multilinear Constraints

The paper introduces LS‑IMP, a two‑stage local‑search algorithm with four novel operators that efficiently solves guaranteed‑delivery advertising inventory allocation under non‑convex multilinear media‑preference constraints, consistently outperforming commercial solvers and heuristics in solution quality and speed on real‑world datasets.

advertisingalgorithminventory allocation

0 likes · 17 min read

Efficient Local Search for Guaranteed Display Advertising Inventory Allocation with Multilinear Constraints

Alimama Tech

Jan 10, 2024 · Artificial Intelligence

Advances in Automated Bidding and Auction Mechanisms for Online Advertising

Advances in automated bidding for online ads have progressed from classic control and linear programming to reinforcement‑learning pipelines, offline and sustainable online RL, and finally generative‑model approaches, each enhancing decision strength, adaptability, and fairness while addressing simulation gaps, multi‑objective constraints, and real‑time efficiency.

auction designautomated biddinggenerative AI

0 likes · 25 min read

Advances in Automated Bidding and Auction Mechanisms for Online Advertising

DataFunTalk

Oct 24, 2023 · Backend Development

Design and Algorithms of an Online Advertising System: Architecture, Coarse Ranking, Budget Control, and Pricing

The article explains the architecture of an online advertising system, detailing the roles of ad placement and playback, the design of coarse ranking modules, budget pacing mechanisms, and pricing algorithms that aim to maximize ROI while controlling costs.

ad rankingadvertisingbackend

0 likes · 14 min read

Design and Algorithms of an Online Advertising System: Architecture, Coarse Ranking, Budget Control, and Pricing

Alimama Tech

Oct 18, 2023 · Artificial Intelligence

Incentive-Compatible Auction Mechanisms for Automated Bidding with Budget and ROI Constraints

The paper presents incentive‑compatible, individually rational auction mechanisms for automated ad bidding where advertisers report private budget and ROI constraints, characterizes feasible allocation and payment rules via monotone budget functions, introduces a personalized ranking‑score auction using a “key ROI,” and demonstrates through experiments that the design achieves near‑optimal welfare and revenue while ensuring truthful reporting.

ROIauction theoryautomated bidding

0 likes · 17 min read

Incentive-Compatible Auction Mechanisms for Automated Bidding with Budget and ROI Constraints

Alimama Tech

Aug 23, 2023 · Artificial Intelligence

Reinforcement Learning for Pacing in Preloaded Ads (RLTP)

The paper introduces RLTP, a reinforcement‑learning‑based pacing system that models delayed‑impression preloaded ads as an MDP, uses a dueling DQN to select traffic probabilities, and simultaneously meets exposure targets, ensures smooth delivery, and maximizes CTR, outperforming rule‑based and PID baselines while removing complex multi‑stage pipelines.

RLTPad pacingdelayed impression

0 likes · 16 min read

Reinforcement Learning for Pacing in Preloaded Ads (RLTP)

Alimama Tech

Aug 16, 2023 · Artificial Intelligence

Personalized Automated Bidding Framework (PerBid) for Fairness‑Aware Online Advertising

PerBid introduces a personalized automated bidding framework that creates context‑aware RL agents for advertiser clusters using a profiling network to embed static and dynamic campaign features, and experiments on Alibaba’s display‑ad platform show up to 10.85% performance gains while markedly improving fairness across heterogeneous advertisers.

automated biddingfairnessonline advertising

0 likes · 23 min read

Personalized Automated Bidding Framework (PerBid) for Fairness‑Aware Online Advertising

Alimama Tech

Apr 19, 2023 · Artificial Intelligence

Potential Generalized Second Price (PGSP) Auction for Augmented Advertising

This paper proposes a two‑stage Potential Generalized Second Price auction for augmented ads, ranking guide ads by expected welfare from their linked second‑step ads, shifting billing to the second click to eliminate free‑riding, and demonstrates via offline and online experiments on Taobao that it boosts click‑through, revenue, and GMV while lowering CPC.

advertisingauctione‑commerce

0 likes · 16 min read

Potential Generalized Second Price (PGSP) Auction for Augmented Advertising

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

0 likes · 20 min read

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

Alimama Tech

Apr 3, 2023 · Artificial Intelligence

AI-Generated Bidding (AIGB): Using Generative Models for Automated Advertising Bidding

AI‑Generated Bidding (AIGB) replaces reinforcement‑learning with a conditional generative model that learns the joint distribution of bids, objectives and constraints from historical trajectories, enabling interpretable, diverse, constraint‑aware bidding strategies that improve efficiency, scalability and explainability for large‑scale advertising platforms.

advertisingautomated biddingconditional modeling

0 likes · 15 min read

AI-Generated Bidding (AIGB): Using Generative Models for Automated Advertising Bidding

Alimama Tech

Mar 29, 2023 · Artificial Intelligence

Advertising Auction Mechanisms: Concepts, Design, and Theory

The article surveys advertising auction mechanisms, explaining game‑theoretic foundations, Myerson’s lemma, welfare‑maximizing designs such as VCG and GSP, revenue‑focused extensions with reserve prices (mGSP, aGSP, rGSP, sGSP), and outlines future research on auto‑bidding, machine‑learning optimization, and generative‑AI impacts.

advertisingauction theorygame theory

0 likes · 38 min read

Advertising Auction Mechanisms: Concepts, Design, and Theory

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

Dec 28, 2022 · Artificial Intelligence

Hierarchically Constrained Adaptive Ad Exposure (HCA2E) for Dynamic Feed Advertising

The Hierarchically Constrained Adaptive Ad Exposure (HCA2E) framework treats each user request as a knapsack item and uses a hierarchical greedy‑plus‑beam‑search optimization with a preservation‑order strategy to jointly maximize platform revenue and user experience while respecting global and per‑request ad‑placement constraints, achieving near‑optimal performance and stable, scalable results in extensive offline and online feed‑advertising experiments.

Knapsack OptimizationReal-time Controldynamic ad placement

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Hierarchically Constrained Adaptive Ad Exposure (HCA2E) for Dynamic Feed Advertising

IEG Growth Platform Technology Team

Nov 22, 2022 · Artificial Intelligence

Advances in Customer Lifetime Value Prediction for Online Advertising: Missing-aware Routing Fusion Network and Cross-domain Adaptive Learning

Two Tencent IEG Growth Platform papers accepted at WSDM 2023 and AAAI 2023 introduce a feature‑missing‑aware routing‑fusion network (MarfNet) and a cross‑domain adaptive framework (CDAF) that significantly improve online advertising LTV prediction despite sparse features and labels.

AICross‑domain AdaptationLTV prediction

0 likes · 4 min read

Advances in Customer Lifetime Value Prediction for Online Advertising: Missing-aware Routing Fusion Network and Cross-domain Adaptive Learning

Xiaohongshu Tech REDtech

Oct 19, 2022 · Artificial Intelligence

Modeling and Optimizing Real‑Time Bidding for Xiaohongshu "Fries" Advertising

Xiaohongshu’s commercial team modeled the real‑time bidding process for its “Fries” ad product, derived an optimal linear‑programming bid formula, and implemented a simple two‑parameter PID‑controlled scheme that meets client pacing, delivery guarantees, and platform profit goals while using practical heuristics.

Real-Time Biddingadvertising optimizationalgorithmic strategy

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Modeling and Optimizing Real‑Time Bidding for Xiaohongshu "Fries" Advertising

Alimama Tech

Sep 21, 2022 · Artificial Intelligence

Alibaba's Three Papers Accepted at NeurIPS 2022

Alibaba’s research team secured three NeurIPS 2022 papers—introducing an Adaptive Parameter Generation network that boosts click‑through rates and revenue, a tuning‑free Global Batch Gradient Aggregation method that speeds recommendation model training by 2.4×, and a Sustainable Online Reinforcement Learning framework that outperforms existing auto‑bidding strategies.

CTR predictionNeurIPSRecommendation systems

0 likes · 6 min read

Alibaba's Three Papers Accepted at NeurIPS 2022

DataFunTalk

Sep 2, 2022 · Artificial Intelligence

Applying Reinforcement Learning to E‑commerce Traffic Control: Practices and Future Directions

This talk by JD Retail's Zhao Yu explains how reinforcement learning is modeled and deployed for large‑scale traffic control during major sales events, detailing system architecture, reward design, offline simulation, model upgrades, and future research directions.

JD.comRL modelinge‑commerce

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Applying Reinforcement Learning to E‑commerce Traffic Control: Practices and Future Directions