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Mar 28, 2024 · Artificial Intelligence

Large-Scale Heterogeneous Graph Modeling and GraphET Engine for Meituan Food Delivery Search Advertising

The paper describes how Meituan’s food‑delivery search advertising uses a heterogeneous billion‑node graph and the GraphET engine to boost weak‑supply recall, detailing a progression from fine‑grained modeling to GPT‑enhanced pre‑training, and presenting a scalable training and low‑latency inference architecture that handles hundreds of billions of edges.

GraphETLarge-Scale GraphMeituan

0 likes · 27 min read

Large-Scale Heterogeneous Graph Modeling and GraphET Engine for Meituan Food Delivery Search Advertising

DataFunTalk

May 27, 2023 · Artificial Intelligence

Graph Algorithms in Alibaba E‑commerce Risk Control: Practices and Insights

The article presents a comprehensive overview of how graph algorithms are applied in Alibaba's e‑commerce risk control system, detailing six sections that include risk scenario introductions, interaction and product content risk methods, dynamic heterogeneous graph practices, a large‑scale competition, and future research directions.

Dynamic GraphRisk Detectione-commerce risk

0 likes · 18 min read

Graph Algorithms in Alibaba E‑commerce Risk Control: Practices and Insights

Ctrip Technology

May 25, 2023 · Artificial Intelligence

Graph-Based Unsupervised Model for Detecting Malicious Account Clusters in Registration Risk Control

This article presents a graph‑neural‑network driven, unsupervised approach that builds heterogeneous user‑feature graphs, learns node weights, constructs user‑user similarity graphs, and applies threshold‑based clustering to identify abnormal registration clusters for fraud detection in Ctrip's business travel platform.

Graph Neural NetworkUnsupervised Learninganomaly detection

0 likes · 12 min read

Graph-Based Unsupervised Model for Detecting Malicious Account Clusters in Registration Risk Control

Meituan Technology Team

Nov 24, 2022 · Artificial Intelligence

Large-Scale Graph Retrieval for Meituan In-Store Advertising: Design, Optimization, and Deployment

The article details Meituan's deployment of large-scale heterogeneous graph recall for in‑store recommendation ads, covering full‑scene graph construction, graph pruning, dynamic negative sampling, spatiotemporal sub‑graph fusion, and performance optimizations that together raise offline hit‑rate by over 5% and online revenue per search by 10‑15%.

Large-Scale TrainingMeituangraph neural networks

0 likes · 25 min read

Large-Scale Graph Retrieval for Meituan In-Store Advertising: Design, Optimization, and Deployment

DataFunTalk

Sep 10, 2022 · Artificial Intelligence

Graph Neural Networks for Recommendation Systems: From Recall to Re‑ranking

This article reviews how graph neural networks are applied across the three stages of recommendation systems—recall, ranking, and re‑ranking—detailing novel models such as NIA‑GCN, GraphSAIL, and DGENN, their experimental improvements, and future research directions.

GNN recallIncremental LearningRecommendation Systems

0 likes · 17 min read

Graph Neural Networks for Recommendation Systems: From Recall to Re‑ranking

Alimama Tech

Jul 6, 2022 · Artificial Intelligence

How Mixed‑Curvature Graph Embeddings Boost E‑commerce Ad Retrieval

This article presents AMCAD, an adaptive mixed‑curvature graph embedding system that models heterogeneous e‑commerce search ad graphs in non‑Euclidean spaces, detailing its sample construction, three‑stage model architecture, offline and online experiments, and demonstrating significant performance gains over Euclidean baselines.

Deep Learningadvertisement retrievale‑commerce

0 likes · 13 min read

How Mixed‑Curvature Graph Embeddings Boost E‑commerce Ad Retrieval

DataFunSummit

Jan 26, 2022 · Artificial Intelligence

Applying Graph Neural Networks for Early Fraud Warning and Malicious URL Detection

This article explains how Tencent's security data lab uses graph neural networks to build heterogeneous temporal graphs for early warning of water‑room fraud cards and to detect malicious URLs, detailing the data modeling, graph construction, attention‑based aggregation, model training, and evaluation results.

AI securityMalicious URL Detectionheterogeneous graph

0 likes · 8 min read

Applying Graph Neural Networks for Early Fraud Warning and Malicious URL Detection

ByteDance Terminal Technology

Jan 11, 2022 · Information Security

Graph-Based Detection of Malicious Webpages: Methods, Experiments, and Future Work

This article presents a comprehensive study on detecting malicious webpages using heterogeneous graph structures and Graph Convolutional Networks, detailing background challenges, technical approaches, model iterations, optimization techniques for large‑scale deployment, experimental results, and directions for future research.

DetectionGCNgraph neural networks

0 likes · 9 min read

Graph-Based Detection of Malicious Webpages: Methods, Experiments, and Future Work

ByteDance Terminal Technology

Jan 7, 2022 · Information Security

Graph-Based Detection of Malicious Webpages: Methods, Experiments, and Future Directions

This article presents a comprehensive study on detecting malicious webpages by constructing heterogeneous graphs from URL redirection and textual features, applying Graph Convolutional Networks and Cluster‑Text‑GCN models, detailing optimization techniques for large‑scale deployment, and outlining future research directions.

GCNgraph neural networksheterogeneous graph

0 likes · 11 min read

Graph-Based Detection of Malicious Webpages: Methods, Experiments, and Future Directions

DataFunSummit

Sep 6, 2021 · Artificial Intelligence

Graph Neural Network‑Based Payment Fraud Detection at eBay

This article explains how eBay uses graph neural networks and a heterogeneous‑graph fraud detection framework (xFraud) to improve payment risk assessment, overcome the limitations of traditional machine‑learning models, and effectively identify both individual and organized fraud in a large‑scale e‑commerce environment.

Dynamic GrapheBayfraud detection

0 likes · 15 min read

Graph Neural Network‑Based Payment Fraud Detection at eBay

DataFunTalk

Apr 16, 2021 · Artificial Intelligence

Live Streaming Recommendation Ranking Model Evolution and Multi‑Objective Learning at Alibaba 1688

This article presents a comprehensive overview of Alibaba's 1688 live‑streaming recommendation system, detailing core challenges such as heterogeneous behavior modeling, multi‑objective optimization, and bias mitigation, and describing four successive model iterations—from feature‑engineered GBDT to attention‑based heterogeneous networks and transformer architectures—along with experimental results and practical insights.

Recommendation SystemsTransformerbias mitigation

0 likes · 22 min read

Live Streaming Recommendation Ranking Model Evolution and Multi‑Objective Learning at Alibaba 1688

iQIYI Technical Product Team

Oct 30, 2020 · Artificial Intelligence

Heterogeneous Mini-Graph Neural Network for Fraud Invitation Detection

HMGNN introduces hyper-nodes to connect many small heterogeneous mini-graphs, uses attention-weighted heterogeneous convolution and residual feature transmission, achieving superior fraud invitation detection on iQIYI and Cora datasets compared to traditional GNNs and other models.

AIGraph Neural NetworkHypergraph

0 likes · 10 min read

Heterogeneous Mini-Graph Neural Network for Fraud Invitation Detection

AntTech

Nov 1, 2018 · Artificial Intelligence

Heterogeneous Graph Neural Networks for Malicious Account Detection (GEM) – Overview of Ant Financial’s CIKM 2018 Paper

This article introduces the GEM method, the first heterogeneous graph neural network designed for malicious account detection, explains the nature and characteristics of malicious accounts, describes why graph neural networks are effective, and presents experimental results from the authors' CIKM 2018 study.

AI securityCIKM 2018financial fraud

0 likes · 8 min read

Heterogeneous Graph Neural Networks for Malicious Account Detection (GEM) – Overview of Ant Financial’s CIKM 2018 Paper

Alibaba Cloud Developer

Jan 12, 2018 · Artificial Intelligence

How Alibaba’s New AI-Powered Ad Retrieval Model Redefined E‑Commerce Sponsored Search

Alibaba’s latest AI-driven ad retrieval framework, unveiled at WWW 2018, replaces keyword‑based search with a user‑behavior heterogeneous graph and machine‑learning models, delivering personalized, high‑efficiency ad matching that boosts ROI for advertisers, improves user experience, and enhances platform revenue.

ad retrievale-commerce advertisingheterogeneous graph

0 likes · 9 min read