How GraphKeeper Tackles Catastrophic Forgetting in Domain‑Incremental Graph Learning

This article analyzes the GraphKeeper framework, which combines multi‑domain graph decoupling, unbiased ridge‑regression knowledge preservation, and a domain‑aware distribution discriminator to overcome catastrophic forgetting in domain‑incremental graph neural network training, and validates its superiority through extensive experiments and ablations.

Catastrophic ForgettingDomain Incremental LearningGraphKeeper

0 likes · 15 min read

How GraphKeeper Tackles Catastrophic Forgetting in Domain‑Incremental Graph Learning

JD Retail Technology

Nov 23, 2023 · Artificial Intelligence

Recent Advances in Advertising Recommendation Algorithms and Their Applications

This article reviews recent progress in advertising recommendation technologies, covering deep learning‑based ranking, sequence modeling, self‑supervised learning, online and reinforcement learning, multimodal recommendation, and fairness, and details four key breakthroughs—data‑driven incremental learning, dynamic group parameter modeling, bilateral interactive graph convolution, and a relation‑aware diffusion model for poster layout generation, along with experimental results and future challenges.

Deep LearningDiffusion ModelsIncremental Learning

0 likes · 25 min read

Recent Advances in Advertising Recommendation Algorithms and Their Applications

Alimama Tech

Sep 14, 2022 · Artificial Intelligence

Streaming Graph Neural Networks via Generative Replay

The paper introduces SGNN‑GR, a framework that pairs a graph neural network with a GAN‑based generative model to replay synthetic historical nodes, enabling continual learning on evolving graphs without storing raw data, achieving near‑retraining accuracy while being 3–6× faster per iteration.

Incremental Learningcontinual learninggenerative replay

0 likes · 10 min read

Streaming Graph Neural Networks via Generative Replay

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

NetEase Cloud Music Tech Team

Mar 9, 2022 · Industry Insights

How NetEase Cloud Music Built a Real‑Time Live‑Stream Recommendation System

This article details the architecture, incremental model training, feature engineering, and deployment strategies that enabled NetEase Cloud Music to achieve real‑time live‑stream recommendation, covering business background, multi‑objective modeling, real‑time feature pipelines, sample attribution, feature admission, and online performance results.

Incremental LearningModel Deploymentfeature engineering

0 likes · 26 min read

How NetEase Cloud Music Built a Real‑Time Live‑Stream Recommendation System

DataFunTalk

Jan 21, 2020 · Artificial Intelligence

How to Enhance Real-Time Updating of Recommendation System Models

The article examines various techniques—including full, incremental, online, and local updates—as well as client‑side embedding refreshes to improve the real‑time performance of recommendation system models, balancing freshness with global optimality.

Incremental LearningOnline LearningRecommendation Systems

0 likes · 9 min read

How to Enhance Real-Time Updating of Recommendation System Models