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Latest from Alimama Tech

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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 DesignGenerative AIautomated bidding

0 likes · 25 min read

Advances in Automated Bidding and Auction Mechanisms for Online Advertising

Alimama Tech

Jan 3, 2024 · Artificial Intelligence

Alimama's 2023 Technical Highlights in AI and Advertising

Alimama’s 2023 newsletter details its AI‑driven advertising breakthroughs, from reinforcement‑learning bidding models and generative pricing (AIGB) to advanced auction mechanisms, historical‑data‑enhanced conversion‑rate prediction, and automated creative generation, highlighting related KDD/MM research papers and production‑level engineering implementations.

AIAlimamaadvertising technology

0 likes · 5 min read

Alimama's 2023 Technical Highlights in AI and Advertising

Alimama Tech

Dec 21, 2023 · Information Security

Alibaba Mama Secure Data Hub: Cloud Architecture and Privacy-Preserving Advertising

Alibaba Mama’s Secure Data Hub delivers a privacy‑enhanced clean‑room for advertising by combining multi‑party computation, federated learning and differential privacy with encrypted operators on a Flink engine, offering cloud‑agnostic, scalable deployment that enables cross‑domain analytics while protecting raw user data and boosting ROI.

Federated LearningPrivacy Computingadvertising analytics

0 likes · 13 min read

Alibaba Mama Secure Data Hub: Cloud Architecture and Privacy-Preserving Advertising

Alimama Tech

Dec 14, 2023 · Artificial Intelligence

AI-Driven Content Risk Control: System Evolution and Optimization at Alibaba

Alibaba Mom’s AI‑driven content risk platform has evolved from simple rule‑matching to a data‑centric, serverless architecture that integrates large‑model acceleration, decision‑tree compilation, high‑throughput vector retrieval and elastic word‑matching, delivering sub‑100 ms text and sub‑1 s image moderation while remaining stable during peak promotional traffic.

AIDevOpscontent moderation

0 likes · 25 min read

AI-Driven Content Risk Control: System Evolution and Optimization at Alibaba

Alimama Tech

Dec 7, 2023 · Industry Insights

How Privacy Computing Enables Cross‑Domain Ad Tracking and Asset Analysis

The 2023 Big Data “Galaxy” case competition highlighted Alibaba Mama and Jiahe Tech’s privacy‑computing based cross‑domain advertising tracking and whole‑domain asset analysis as an outstanding example, detailing the Secure Data Hub platform, its use of multi‑party secure computation, federated learning, differential privacy, and the resulting improvements in data flow, compliance, and marketing efficiency.

Privacy Computingadvertising analyticscross-domain tracking

0 likes · 6 min read

How Privacy Computing Enables Cross‑Domain Ad Tracking and Asset Analysis

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.

AIAuction Designauto-bidding

0 likes · 38 min read

Evolution of Alibaba's AI-Driven Advertising Decision Technologies

Alimama Tech

Nov 22, 2023 · Artificial Intelligence

Robust Link Prediction under Bilateral Edge Noise via Robust Graph Information Bottleneck (RGIB)

The paper introduces Robust Graph Information Bottleneck (RGIB), a framework that jointly mitigates bilateral edge noise in link prediction by decoupling topology, label, and representation information, with two variants (RGIB‑SSL and RGIB‑REP) that achieve up to 12.9% AUC gains on benchmarks and have already boosted click‑through‑rate robustness and revenue in Alibaba’s advertising system.

RGIBbilateral noisegraph neural networks

0 likes · 13 min read

Robust Link Prediction under Bilateral Edge Noise via Robust Graph Information Bottleneck (RGIB)

Alimama Tech

Nov 15, 2023 · Industry Insights

How Alibaba’s Data‑Driven Marketing Drove Record‑Breaking Double 11 Results

The 2023 Tmall Double 11 campaign saw Alibaba’s data‑intelligent tools boost market share to 63.14%, lift GMV by over 6%, and help more than 200 brands achieve double‑digit growth through platforms like ShowMAX, Super Live, and Wanxiang AI, illustrating the power of integrated digital marketing in e‑commerce.

AlibabaCase StudyDigital Marketing

0 likes · 11 min read

How Alibaba’s Data‑Driven Marketing Drove Record‑Breaking Double 11 Results

Alimama Tech

Nov 15, 2023 · Artificial Intelligence

Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking (HC²)

The HC² framework enhances multi‑scenario ad ranking by jointly applying a generalized contrastive loss on shared representations and an individual contrastive loss on scenario‑specific layers, using label‑aware positive sampling, diffusion‑noise negative sampling, and inverse‑similarity weighting, achieving consistent offline gains and up to 2.5% CVR and 3.7% GMV improvements in Alibaba’s live system.

Recommendation Systemsad rankingcontrastive learning

0 likes · 16 min read

Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking (HC²)

Alimama Tech

Nov 1, 2023 · Artificial Intelligence

BOMGraph: Boosting Multi-Scenario E-commerce Search with a Unified Graph Neural Network

BOMGraph introduces a unified heterogeneous graph neural network that jointly models text, image, and similar‑item search across multiple e‑commerce scenarios, using meta‑path‑guided attention, disentangled scenario‑specific and shared embeddings, and contrastive learning to alleviate sample sparsity, achieving consistent offline and online performance gains.

contrastive learninge-commercegraph neural network

0 likes · 13 min read