Tagged articles

Industrial Deployment

4 articles · Page 1 of 1

Jul 3, 2026 · Artificial Intelligence

Kuaishou’s Recommendation-as-Generation Shatters Content Limits, Boosts Ads

RaG (Recommendation-as-Generation) redefines short‑video recommendation by predicting user interests, converting them into discrete semantic IDs, and generating personalized ads via a multi‑agent pipeline, achieving industrial‑scale deployment for over 400 million daily users and delivering a 1.87 % lift in ad revenue.

D-SIDsGenerative Recommendation ModelIndustrial Deployment

0 likes · 14 min read

Kuaishou’s Recommendation-as-Generation Shatters Content Limits, Boosts Ads

DeWu Technology

Jan 21, 2026 · Artificial Intelligence

Breaking the Recommendation Feedback Loop with LLM‑Powered Dynamic User Knowledge Graphs

By integrating large language models to dynamically construct user knowledge graphs and applying two‑hop reasoning, the authors enhance serendipity in a large‑scale e‑commerce community recommendation system, achieving significant online gains in diversity, novelty, and user engagement metrics.

Industrial DeploymentLLMSerendipity

0 likes · 17 min read

Breaking the Recommendation Feedback Loop with LLM‑Powered Dynamic User Knowledge Graphs

Tencent Cloud Developer

Dec 4, 2025 · Artificial Intelligence

From Tapestry to LLMs: 30+ Years of Recommender System Evolution

This article traces the three‑decade evolution of recommender systems—from early collaborative‑filtering prototypes like Tapestry, through the Netflix Prize era and deep‑learning breakthroughs such as Wide&Deep and DIN, to the current generative‑AI wave driven by large language models—highlighting key milestones, technical shifts, industrial deployments, and future challenges.

Collaborative FilteringIndustrial DeploymentLarge Language Models

0 likes · 38 min read

From Tapestry to LLMs: 30+ Years of Recommender System Evolution

Baobao Algorithm Notes

Oct 13, 2024 · Artificial Intelligence

Can Hierarchical LLMs Transform Sequential Recommendation? A Deep Dive

This article provides a comprehensive analysis of the HLLM paper, detailing its hierarchical LLM architecture for item and user modeling, the training objectives, fusion strategies, extensive offline and online experiments, scaling behavior, ablation studies, and practical deployment insights in large‑scale recommendation systems.

Industrial DeploymentLLMSequential Modeling

0 likes · 12 min read

Can Hierarchical LLMs Transform Sequential Recommendation? A Deep Dive