news recommendation

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.

AutomationR&D managementTesting

0 likes · 16 min read

Testing Challenges and Quality Assurance Improvements for News Recommendation Systems

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.

Graph Neural Networksdeep learningexplainability

0 likes · 23 min read

Combining Knowledge Graphs with Personalized News Recommendation Systems

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.

Graph Constructiondeep learningexplainability

0 likes · 22 min read

Personalized News Recommendation System Based on Knowledge Graphs

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

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

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.

AIRecommendation systemsdeep learning

0 likes · 11 min read

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

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

Sep 5, 2018 · Artificial Intelligence

Reinforcement Learning Theory Overview and Its Application to News Recommendation

This article reviews reinforcement learning fundamentals, contrasts it with supervised learning, surveys major RL algorithms such as DDPG and DQN, and details how these methods can be modeled for sequential news recommendation, including system architecture, state‑action definitions, and practical challenges.

AIDDPGDQN

0 likes · 15 min read

Reinforcement Learning Theory Overview and Its Application to News Recommendation

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.

Feature EngineeringRankingcollaborative filtering

0 likes · 14 min read

News Recommendation Algorithms: Architecture, Recall, and Ranking Techniques

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.

Search Enginemachine learningmulti-objective optimization

0 likes · 5 min read