graph representation learning

DataFunTalk

Sep 2, 2024 · Artificial Intelligence

Exploring Graph Foundation Models: Concepts, Techniques, and Future Directions

This article introduces graph foundation models, explains their relationship with large language models, reviews recent advances in graph neural networks and representation learning, presents the authors' own research on PT‑HGNN, Specformer and GraphTranslator, and discusses challenges, future research directions, and a Q&A session.

Artificial IntelligenceFoundation ModelsGraph Neural Networks

0 likes · 23 min read

Exploring Graph Foundation Models: Concepts, Techniques, and Future Directions

DataFunTalk

Jul 9, 2024 · Artificial Intelligence

Graph Knowledge Transfer and the Knowledge Bridge Learning Framework

This article presents an overview of graph knowledge transfer, discussing the data‑hungry problem, distribution shift in graph data, the Knowledge Bridge Learning (KBL) paradigm, the Bridged‑GNN implementation, experimental results across multiple scenarios, and future research directions.

Graph Neural Networksbridged-GNNdomain adaptation

0 likes · 19 min read

Graph Knowledge Transfer and the Knowledge Bridge Learning Framework

DataFunSummit

Jun 1, 2024 · Artificial Intelligence

Graph Foundation Models: Concepts, Progress, and Future Directions

This article provides a comprehensive overview of Graph Foundation Models (GFMs), covering their definition, key characteristics, historical development of graph machine learning, recent research trends such as PT‑HGNN, Specformer, and GraphTranslator, and discusses future challenges and research directions.

Artificial IntelligenceFoundation ModelsGraph Neural Networks

0 likes · 23 min read

Graph Foundation Models: Concepts, Progress, and Future Directions

DataFunSummit

Jan 12, 2024 · Artificial Intelligence

Application of Graph Neural Networks in Recommendation Systems: OPPO Business Scenario Practice

This article explains the fundamentals of graph neural networks and graph representation learning, outlines how graphs enhance recommendation systems, and details OPPO's practical implementation of a hybrid dual‑tower and graph sub‑network model to improve recall and ranking performance.

CTR predictionGraph Neural NetworksOPPO

0 likes · 19 min read

Application of Graph Neural Networks in Recommendation Systems: OPPO Business Scenario Practice

DataFunTalk

Jul 16, 2023 · Artificial Intelligence

Application of Graph Neural Networks in Recommendation Systems: OPPO Business Scenario Practice

This article introduces graph neural networks, explains graph representation learning, discusses their evolution from random walks to spectral and spatial convolutions, and details how OPPO applies GNNs to improve recommendation system recall and ranking, highlighting practical architecture, experimental gains, and future research directions.

Graph Neural NetworksOPPORecommendation systems

0 likes · 19 min read

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

AntTech

May 25, 2018 · Artificial Intelligence

Insights from AAAI 2018: Conference Overview, Paper Highlights, and Ant Financial Contributions

The article provides a comprehensive overview of the AAAI 2018 conference, including submission statistics, country rankings, popular research tracks, award-winning papers, detailed summaries of notable AI papers such as GraphGAN, HARP, PrivSR, and domain adaptation, as well as Ant Financial's own contributions like cw2vec and privacy‑preserving recommendation systems.

AAAI 2018Artificial IntelligencePaper Summaries

0 likes · 15 min read

Insights from AAAI 2018: Conference Overview, Paper Highlights, and Ant Financial Contributions

AntTech

Dec 20, 2017 · Artificial Intelligence

Network Embedding Overview and Recent Research Directions from CIKM 2017

An overview of network embedding presented at CIKM 2017, covering its definition, loss functions, algorithm categories such as spectral methods, random walks, deep learning models, emerging research topics like dynamic and attributed embeddings, and various application scenarios illustrated with numerous academic papers.

CIKM2017attribute integrationdeep learning

0 likes · 9 min read

Network Embedding Overview and Recent Research Directions from CIKM 2017