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DataFunTalk
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.

foundation-modelsgraph representation learninglarge language models
0 likes · 23 min read
Exploring Graph Foundation Models: Concepts, Techniques, and Future Directions
DataFunTalk
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.

Knowledge Transferbridged-GNNdomain adaptation
0 likes · 19 min read
Graph Knowledge Transfer and the Knowledge Bridge Learning Framework
DataFunSummit
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.

foundation-modelsgraph neural networksgraph representation learning
0 likes · 23 min read
Graph Foundation Models: Concepts, Progress, and Future Directions
DataFunTalk
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.

OPPORecommendation Systemsgraph neural networks
0 likes · 19 min read
Application of Graph Neural Networks in Recommendation Systems: OPPO Business Scenario Practice
DaTaobao Tech
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.

DICEGNNgraph representation learning
0 likes · 17 min read
Graph Neural Network Based Content Recall and Popularity Bias Mitigation for Alibaba's Home‑Decor Platform
Meituan Technology Team
Meituan Technology Team
Aug 27, 2020 · Artificial Intelligence

Automated Graph Representation Learning for KDD Cup 2020 AutoGraph: Technical Solution and Advertising Applications

The team built an automated graph learning framework that preprocesses diverse graphs, employs four GNN architectures, conducts rapid hyper‑parameter tuning, and fuses models with density‑aware weighting, securing first place in KDD Cup 2020 AutoGraph and boosting Meituan’s ad recall and CTR prediction.

AutoMLKDD Cupgraph neural networks
0 likes · 30 min read
Automated Graph Representation Learning for KDD Cup 2020 AutoGraph: Technical Solution and Advertising Applications
AntTech
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 2018Privacy-Preserving Recommendationartificial intelligence
0 likes · 15 min read
Insights from AAAI 2018: Conference Overview, Paper Highlights, and Ant Financial Contributions