A5101987413- Recommender Systems and Techniques
- Caching and Content Delivery
- Advanced Graph Neural Networks
- Advanced Image and Video Retrieval Techniques
Baidu (China)
2023-2024
While having been used widely for large-scale recommendation and online advertising, the Graph Neural Network (GNN) has demonstrated its representation learning capacity to extract embeddings of nodes edges through passing, transforming, aggregating information over graph. In this work, we propose PGLBox1 - a multi-GPU graph framework based on PaddlePaddle [24], incorporating with optimized storage, computation, communication strategies, train deep GNNs web-scale graphs recommendation....
Graph Neural Networks (GNNs) have become critical in various domains such as online advertising but face scalability challenges due to the growing size of graph data, leading needs for advanced distributed GPU computation strategies across multiple nodes. This paper presents PGLBox-Cluster, a robust learning framework constructed atop PaddlePaddle platform, implemented efficiently process graphs comprising billions nodes and edges. Through strategic partitioning model, node attributes, data...