Tagged articles

incremental learning

8 articles · Page 1 of 1

Jul 13, 2026 · Artificial Intelligence

Inside JD’s Oxygen AIIC: An Industrial‑Scale LLM/VLM‑Powered Product Knowledge Platform for Billions of SKUs

JD’s Oxygen AIIC combines human‑in‑the‑loop ontology engineering, a semantic search‑then‑discrimination pipeline, and a self‑evolving multi‑task LLM/VLM model to produce high‑quality product knowledge for over a hundred thousand categories and billions of daily SKU updates, boosting search coverage to 80%, attribute auto‑fill to over 80%, cutting quality issues by 37% and raising click‑through by 9% while achieving 94.2% precision and 82.8% recall.

JD.comKnowledge GraphLLM

0 likes · 21 min read

Inside JD’s Oxygen AIIC: An Industrial‑Scale LLM/VLM‑Powered Product Knowledge Platform for Billions of SKUs

JD Retail Technology

Jul 6, 2026 · Artificial Intelligence

GROLE: Instance-Level Expert Routing for Incremental Learning in Oxygen AIIC Models

The paper introduces GROLE, a two‑stage incremental‑learning framework that builds a frozen pool of task‑specific LoRA experts and trains a lightweight instance‑level selector via reinforcement‑learning‑based gradient‑free optimization with Dirichlet sampling, achieving superior stability‑plasticity trade‑offs and state‑of‑the‑art results on multiple CL benchmarks.

GROLELarge Language ModelsLoRA

0 likes · 14 min read

GROLE: Instance-Level Expert Routing for Incremental Learning in Oxygen AIIC Models

AI Frontier Lectures

Jan 12, 2026 · Artificial Intelligence

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.

Domain Incremental LearningGraphKeeperKnowledge Preservation

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.

advertising recommendationdeep learningdiffusion models

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.

continual learninggenerative replayincremental learning

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 recallRankingRecommendation 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.

Industry InsightsModel 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.

AIRecommendation Systemsincremental learning

0 likes · 9 min read

How to Enhance Real-Time Updating of Recommendation System Models