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NetEase Media Technology Team
NetEase Media Technology Team
Jun 12, 2020 · Artificial Intelligence

Semantic Text Understanding for NetEase News Feed Recommendation

NetEase improves its news‑feed recommendation by applying a multi‑stage semantic text understanding pipeline—lexical analysis, hierarchical content tagging, and quality filtering—using two‑level classifiers, LDA‑based topic modeling, multi‑label concept and entity extraction, and dense vector representations to better capture user interests and boost personalization performance.

NLPRecommendation Systemsfeature engineering
0 likes · 9 min read
Semantic Text Understanding for NetEase News Feed Recommendation
DataFunTalk
DataFunTalk
Mar 16, 2020 · Artificial Intelligence

Phoenix News Feed Recommendation System: Architecture, Modeling, and Feature Engineering

This article presents a comprehensive overview of Phoenix News's AI‑driven feed recommendation system, detailing its business challenges, multi‑stage architecture, deep learning models, feature pipelines, metric trade‑offs, cold‑start solutions, and practical insights for improving user satisfaction and content quality.

AIDeep Learningfeature engineering
0 likes · 22 min read
Phoenix News Feed Recommendation System: Architecture, Modeling, and Feature Engineering
21CTO
21CTO
Mar 4, 2016 · Artificial Intelligence

How Facebook’s News Feed Works: Architecture, Culture, and Ranking Secrets

This article shares insights from former Facebook engineers on the company’s engineering culture, open workspace, code‑review practices, and the technical architecture behind the News Feed, including real‑time publishing, push/pull models, and machine‑learning‑driven ranking.

FacebookSystem Architecturenews feed
0 likes · 10 min read
How Facebook’s News Feed Works: Architecture, Culture, and Ranking Secrets