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NetEase Media Technology Team
NetEase Media Technology Team
Dec 6, 2022 · R&D Management

Testing Challenges and Quality Assurance Improvements for News Recommendation Systems

To cope with the multi‑stage, feature‑rich and rapidly iterated NetEase News recommendation pipeline, the QA team introduced detailed stage logging, controllable recall and user‑profile injection, configurable filters, forced‑push mechanisms, an integrated performance‑testing platform, automated case configuration, centralized requirement tracking, and self‑test tools, dramatically boosting testing quality and efficiency while outlining future automation goals.

AutomationPerformance TestingR&D management
0 likes · 16 min read
Testing Challenges and Quality Assurance Improvements for News Recommendation Systems
DataFunSummit
DataFunSummit
Nov 30, 2022 · Artificial Intelligence

Combining Knowledge Graphs with Personalized News Recommendation Systems

This article presents a comprehensive overview of a personalized news recommendation system that leverages knowledge graphs to improve accuracy, explainability, and user satisfaction, detailing background motivations, graph construction methods, model architecture, experimental results, and practical insights from a Meituan research perspective.

Deep Learningexplainabilitygraph neural networks
0 likes · 23 min read
Combining Knowledge Graphs with Personalized News Recommendation Systems
DataFunTalk
DataFunTalk
Sep 25, 2022 · Artificial Intelligence

Personalized News Recommendation System Based on Knowledge Graphs

This talk presents a personalized news recommendation system that leverages knowledge graphs to enhance recommendation accuracy, explainability, and user interest modeling, detailing background, graph construction methods, multi‑task deep learning architecture, experimental results, and future research directions.

Deep LearningGraph ConstructionKnowledge Graph
0 likes · 22 min read
Personalized News Recommendation System Based on Knowledge Graphs
DataFunTalk
DataFunTalk
May 22, 2022 · Artificial Intelligence

Advances in Information‑Flow Recommendation: Pre‑trained Models and Multimodal User‑Interface Modeling

This article reviews Huawei Noah's Ark Lab's work on modern information‑flow recommendation, covering the evolution from collaborative filtering to deep learning, the application of BERT‑based pre‑training for news ranking, multimodal user‑interface modeling, practical deployment challenges, and future research directions.

AIBERTHuawei
0 likes · 19 min read
Advances in Information‑Flow Recommendation: Pre‑trained Models and Multimodal User‑Interface Modeling
IT Architects Alliance
IT Architects Alliance
Apr 23, 2021 · Industry Insights

Inside Toutiao’s Massive Scale: How the News App Handles Billions of Requests

This article provides an in‑depth technical overview of Toutiao’s rapid growth, data collection pipelines, user modeling, cold‑start strategies, recommendation engine architecture, storage solutions, push notification system, microservice design, and its three‑layer PaaS platform, illustrating how the news app serves hundreds of millions of users daily.

Big DataSystem ArchitectureToutiao
0 likes · 8 min read
Inside Toutiao’s Massive Scale: How the News App Handles Billions of Requests
DataFunTalk
DataFunTalk
Aug 22, 2020 · Artificial Intelligence

Dual Cold-Start News Recommendation via Neighborhood-Based Transfer Learning

This article presents a Neighborhood‑based Transfer Learning approach to solve the Dual Cold‑Start Recommendation problem in news services by transferring app‑installation similarity knowledge and using category‑level preferences to recommend unseen articles to brand‑new users.

AIcold startneighborhood
0 likes · 8 min read
Dual Cold-Start News Recommendation via Neighborhood-Based Transfer Learning
DataFunTalk
DataFunTalk
Nov 26, 2019 · Artificial Intelligence

Neural News Recommendation with Attentive Multi‑View Learning and Personalized Attention

This article surveys two neural news recommendation approaches—NAML, which uses multi‑view learning to fuse heterogeneous news information, and NPA, which incorporates personalized attention for both words and news items—demonstrating their superior performance over strong baselines on real‑world MSN news data through extensive experiments and visual analyses.

AIDeep LearningRecommendation Systems
0 likes · 11 min read
Neural News Recommendation with Attentive Multi‑View Learning and Personalized Attention
Sohu Tech Products
Sohu Tech Products
Oct 10, 2018 · Artificial Intelligence

Optimizing News Recall with DDPG Reinforcement Learning and Transformer Architecture

This article explains how reinforcement learning, specifically the DDPG algorithm combined with Transformer-based networks, is applied to improve large‑scale news recall systems, detailing the business scenario, algorithm selection, model architecture, speed optimizations, training challenges, and observed online performance gains.

AIDDPGTransformer
0 likes · 13 min read
Optimizing News Recall with DDPG Reinforcement Learning and Transformer Architecture
Sohu Tech Products
Sohu Tech Products
Aug 29, 2018 · Artificial Intelligence

News Recommendation Algorithms: Architecture, Recall, and Ranking Techniques

This article explains the architecture of news recommendation systems, detailing the two-stage recall and ranking process, various recall methods such as content‑based, collaborative filtering and matrix factorization, and advanced ranking models including LR, GBDT, FM, and wide‑and‑deep DNNs.

collaborative filteringmachine learningnews recommendation
0 likes · 14 min read
News Recommendation Algorithms: Architecture, Recall, and Ranking Techniques
Architects Research Society
Architects Research Society
Dec 17, 2015 · Artificial Intelligence

How Search Engine Experience Informs Personalized Recommendation at Toutiao

The article explains how search engine techniques such as large‑scale candidate recall, fine‑grained ranking, user profiling, and multi‑objective optimization are applied to news personalization at Toutiao, highlighting data sampling, machine‑learning pipelines, challenges of news freshness, and architectural evolution.

multi-objective optimizationnews recommendationrecommendation
0 likes · 5 min read
How Search Engine Experience Informs Personalized Recommendation at Toutiao