GNN | BestHub

DataFunTalk

Feb 6, 2025 · Artificial Intelligence

Why Graph Neural Networks Are Suitable for Recommendation Systems

Graph Neural Networks excel in recommendation systems because they can model complex user‑item relationships, capture high‑order interactions, adapt dynamically to real‑time behavior, propagate multi‑step information, enrich contextual embeddings, alleviate data sparsity, and improve long‑tail item coverage, with practical e‑commerce case studies available for download.

Artificial IntelligenceE-commerceGNN

0 likes · 5 min read

Why Graph Neural Networks Are Suitable for Recommendation Systems

DataFunSummit

Jun 4, 2024 · Artificial Intelligence

Multimodal and Graph Neural Network Techniques for eBay Recommendation Systems

This article details eBay's practical experience integrating multimodal data and graph neural networks into its recommendation pipeline, covering pain‑point analysis, a twin‑tower multimodal embedding model with triplet loss and TransH, engineering design, experimental results, and key takeaways for future AI‑driven product development.

GNNeBayembedding

0 likes · 19 min read

Multimodal and Graph Neural Network Techniques for eBay Recommendation Systems

DataFunTalk

Sep 29, 2023 · Artificial Intelligence

Edge‑Cloud Collaborative Graph Neural Network Recommendation Systems: Architecture, Personalization, Model Compression, and Security

This article reviews the evolution of underlying compute power for GNN‑based recommendation systems, explores edge‑side personalization, describes cloud‑edge collaborative implementations, discusses model compression and deployment strategies, and highlights security challenges of deploying GNN models on end devices.

Edge ComputingGNNSecurity

0 likes · 11 min read

Edge‑Cloud Collaborative Graph Neural Network Recommendation Systems: Architecture, Personalization, Model Compression, and Security

DataFunSummit

May 25, 2023 · Artificial Intelligence

Edge‑Cloud Perspectives on Graph Neural Network‑Based Recommendation Systems

From an edge‑cloud viewpoint, this article examines the feasibility of deploying graph neural network (GNN) recommendation systems on devices, covering underlying compute evolution, personalization, edge‑cloud collaboration, model compression, deployment strategies, and security challenges, while referencing recent research advances.

AIEdge ComputingGNN

0 likes · 12 min read

Edge‑Cloud Perspectives on Graph Neural Network‑Based Recommendation Systems

DataFunTalk

Dec 28, 2022 · Artificial Intelligence

A Comprehensive Survey of Graph Neural Networks: Development, Complex Graph Models, Applications, Scalability, and Future Directions

This article provides an extensive overview of graph neural networks, tracing their evolution from early RNN‑based models to modern message‑passing frameworks, discussing complex graph types, diverse real‑world applications, scalability challenges, design spaces, training platforms, and promising research directions.

GNNGraph Neural Networksdeep learning

0 likes · 49 min read

A Comprehensive Survey of Graph Neural Networks: Development, Complex Graph Models, Applications, Scalability, and Future Directions

DataFunTalk

Sep 18, 2022 · Artificial Intelligence

Applying Graph Machine Learning in Ant Group's Recommendation System

This article presents how Ant Group leverages graph machine learning, including knowledge graph, social network, and cross-domain techniques, to enhance recommendation for low-activity users across scenarios such as fund, coupon, and waistband recommendations, detailing model architecture, challenges, solutions, and experimental results.

GNNKnowledge Graphgraph learning

0 likes · 13 min read

Applying Graph Machine Learning in Ant Group's Recommendation System

DaTaobao Tech

Mar 10, 2022 · Artificial Intelligence

Graph Neural Network Based Content Recall and Popularity Bias Mitigation for Alibaba's Home‑Decor Platform

The paper presents Alibaba’s home‑decor platform solution that combines graph‑neural‑network side‑information mining and a multi‑view GNN framework with the DICE causal embedding approach to alleviate sparse user behavior and popularity bias, achieving higher recall accuracy and diversity as demonstrated by offline metrics and online A/B test improvements.

DICEGNNRecommendation

0 likes · 17 min read

Graph Neural Network Based Content Recall and Popularity Bias Mitigation for Alibaba's Home‑Decor Platform

DataFunSummit

Jan 9, 2022 · Artificial Intelligence

Applying Graph Neural Networks to Fraud Detection: Background, Research Progress, Methods, and Resources

This article reviews the fundamentals of fraud, surveys the evolution of graph neural network research for fraud detection, outlines practical application steps, discusses key challenges such as disguise, scalability, and label scarcity, and provides representative papers, new research directions, industrial case studies, and open-source resources.

AIGNNGraph Neural Networks

0 likes · 23 min read

Applying Graph Neural Networks to Fraud Detection: Background, Research Progress, Methods, and Resources

DataFunTalk

Nov 11, 2021 · Artificial Intelligence

Applying Graph Neural Networks for Financial Risk Control: A Case Study by Shuhe Technology

This article details how Shuhe Technology leveraged large‑scale graph neural networks, built with DGL and PyTorch, to improve financial fraud detection by preparing massive relationship graphs, pruning sparse nodes, extracting rich features, addressing class imbalance, and achieving a stable AUC gain of about four points.

DGLGATGNN

0 likes · 12 min read

Applying Graph Neural Networks for Financial Risk Control: A Case Study by Shuhe Technology

DataFunSummit

Nov 10, 2021 · Artificial Intelligence

Applying Graph Neural Networks for Financial Risk Control: A Case Study by Shuhe Technology

This article describes how Shuhe Technology leveraged graph neural networks to improve financial risk assessment by preparing massive relational graph data, selecting DGL as the development framework, designing a GraphSage‑GAT model, addressing data sparsity and imbalance, and achieving notable AUC gains over traditional methods.

AIGNNGraph Neural Networks

0 likes · 12 min read

DataFunTalk

Oct 10, 2021 · Artificial Intelligence

Adaptive Universal Generalized PageRank Graph Neural Network (GPR‑GNN): Overview, Challenges, and Experimental Insights

This article presents an in‑depth overview of the Adaptive Universal Generalized PageRank Graph Neural Network (GPR‑GNN), explains the two main limitations of conventional GNNs—lack of generality across homophilic and heterophilic graphs and over‑smoothing—describes the GPR‑GNN architecture with learnable propagation weights, and summarizes synthetic and real‑world experiments that demonstrate its superior generality, resistance to over‑smoothing, interpretability, and potential future extensions.

GNNGeneralizationGeneralized PageRank

0 likes · 18 min read

Adaptive Universal Generalized PageRank Graph Neural Network (GPR‑GNN): Overview, Challenges, and Experimental Insights

DataFunTalk

Aug 19, 2021 · Artificial Intelligence

Graph Computing for Risk Control in WeChat Pay: Platforms, Algorithms, and Practices

This talk presents how WeChat Pay leverages graph computing, including graph databases, engines, and algorithms such as GNN and PageRank, to combat fraud and money‑laundering by shifting from individual feature engineering to network‑level analysis, highlighting platform choices, practical experiences, and technology‑for‑good outcomes.

GNNGraph ComputingWeChat Pay

0 likes · 16 min read

Graph Computing for Risk Control in WeChat Pay: Platforms, Algorithms, and Practices

DataFunTalk

Mar 23, 2021 · Artificial Intelligence

Explainability in Graph Neural Networks: A Taxonomic Survey

This article surveys recent advances in graph neural network explainability, systematically categorizing instance‑level and model‑level methods, reviewing datasets, evaluation metrics, and proposing new benchmark graph datasets for interpretable GNN research, and highlighting future research directions.

GNNGraph Neural NetworksInterpretability

0 likes · 40 min read

Explainability in Graph Neural Networks: A Taxonomic Survey

DataFunTalk

Feb 25, 2021 · Artificial Intelligence

Applying Graph Embedding and Vector Recall for Personalized Recommendation in a UGC Community

This article describes how a UGC app tackled user and content cold‑start problems by introducing a personalized vector‑recall pipeline based on network representation learning and multimodal embeddings, detailing graph construction, GraphSAGE and GAT implementations, offline experiments, A/B test results, and future directions.

GNNgraph embeddingmultimodal

0 likes · 14 min read

Applying Graph Embedding and Vector Recall for Personalized Recommendation in a UGC Community

AntTech

Feb 24, 2021 · Artificial Intelligence

Ant Group's Self‑Developed Graph Neural Network Research: GeniePath and Bandit Sampler

This article introduces the fundamentals of graph neural networks, explains their expressive power for relational risk identification, and details Ant Group's innovations—including the GeniePath architecture and a bandit‑based sampling optimizer—that achieve superior performance on benchmark datasets.

GNNGeniePathGraph Neural Networks

0 likes · 7 min read

Ant Group's Self‑Developed Graph Neural Network Research: GeniePath and Bandit Sampler