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Evaluation

213 articles · Page 3 of 3
DataFunTalk
DataFunTalk
Aug 30, 2019 · Artificial Intelligence

TransFM: Integrating Translation-based Recommendation and Factorization Machines for Sequential Recommendation

This article reviews the TransFM model, which combines the translation‑based sequential recommendation approach (TransRec) with factorization machines (FM), explains its formulation, optimization via sequential Bayesian personalized ranking, and demonstrates its superior performance on Amazon and Google Local datasets compared with several baselines.

Evaluationfactorization machinesmachine learning
0 likes · 8 min read
TransFM: Integrating Translation-based Recommendation and Factorization Machines for Sequential Recommendation
iQIYI Technical Product Team
iQIYI Technical Product Team
Jul 12, 2019 · Artificial Intelligence

Real-Time Evaluation System for Adaptive Bitrate (ABR) Algorithms and Controlled Bitrate Distribution

RESA is a real‑time evaluation platform that continuously tests multiple Adaptive Bitrate (ABR) algorithms on live user traffic, introduces a multi‑user QoE metric derived from viewing behavior, reveals trade‑offs between clarity and bandwidth, and proposes the RL‑based ABSbc algorithm to steer bitrate distribution and balance user experience with network cost.

ABRBandwidth ControlEvaluation
0 likes · 23 min read
Real-Time Evaluation System for Adaptive Bitrate (ABR) Algorithms and Controlled Bitrate Distribution
21CTO
21CTO
Jan 16, 2019 · Artificial Intelligence

Inside Toutiao’s Recommendation Engine: Architecture, Features, and Evaluation

This article provides a comprehensive overview of Toutiao’s recommendation system, detailing its three‑dimensional modeling of content, user, and context, the feature extraction pipeline, real‑time training infrastructure, user‑tag generation, evaluation methodology, and content‑safety mechanisms.

Content SafetyEvaluationReal-time Training
0 likes · 18 min read
Inside Toutiao’s Recommendation Engine: Architecture, Features, and Evaluation
DataFunTalk
DataFunTalk
Jan 3, 2019 · Artificial Intelligence

Machine Learning and Recommendation System Practice

This article presents a comprehensive overview of applying machine learning to recommendation systems, covering fundamental challenges such as user cold‑start, precise interest modeling, collaborative filtering, and both offline and online evaluation methods, while illustrating concepts with numerous diagrams.

AIEvaluationRecommendation Systems
0 likes · 9 min read
Machine Learning and Recommendation System Practice
360 Quality & Efficiency
360 Quality & Efficiency
May 11, 2018 · Artificial Intelligence

Common Engineering Algorithms and Their Testing Methods

This article introduces the most commonly used algorithms in engineering—recommendation, optimization, estimation, and classification—explains their typical application scenarios, and discusses various testing methods and evaluation metrics such as offline experiments, user surveys, A/B testing, and performance indicators like accuracy, coverage, diversity, and robustness.

EvaluationOptimizationalgorithm
0 likes · 12 min read
Common Engineering Algorithms and Their Testing Methods
Efficient Ops
Efficient Ops
Feb 25, 2018 · Cloud Computing

Why Multi-Cloud Management Platforms Need Standards: Key Insights & Evaluation

Amid the rise of multi‑cloud strategies, China’s nascent multi‑cloud management platform market faces a lack of standards, prompting TrustCloud to launch the first domestic evaluation framework that assesses information authenticity, platform quality, and service completeness, with results to be revealed at the 2018 Cloud Computing Open‑Source Industry Conference.

Cloud ComputingCloud ManagementEvaluation
0 likes · 6 min read
Why Multi-Cloud Management Platforms Need Standards: Key Insights & Evaluation
Architecture Digest
Architecture Digest
Jan 30, 2018 · Artificial Intelligence

Overview of Toutiao's Recommendation System: Architecture, Content Analysis, User Tagging, Evaluation, and Content Safety

This article presents a comprehensive overview of Toutiao's recommendation system, detailing its three‑dimensional modeling approach, real‑time training pipeline, feature engineering, content and user analysis techniques, evaluation methodology, and the extensive content‑safety mechanisms employed to ensure reliable and responsible information distribution.

Content SafetyEvaluationcontent analysis
0 likes · 19 min read
Overview of Toutiao's Recommendation System: Architecture, Content Analysis, User Tagging, Evaluation, and Content Safety
21CTO
21CTO
Jan 16, 2018 · Artificial Intelligence

Inside Toutiao’s Recommendation Engine: Architecture, Features, and Evaluation

This article provides a comprehensive overview of Toutiao's recommendation system, covering its three‑dimensional modeling approach, feature engineering, real‑time training pipeline, recall strategies, user‑tag generation, evaluation methodology, and content‑safety mechanisms.

Content SafetyEvaluationReal-time Training
0 likes · 18 min read
Inside Toutiao’s Recommendation Engine: Architecture, Features, and Evaluation
Architecture Digest
Architecture Digest
Sep 15, 2017 · Artificial Intelligence

Overview of Recommendation Systems: Goals, Methods, Architecture, and Practical Considerations

This article explains the objectives of recommendation systems, compares popular recommendation approaches, details the components and algorithms of personalized recommendation pipelines, and discusses practical challenges such as real‑time processing, freshness, cold‑start, diversity, content quality, and surprise handling.

EvaluationReal-timecold-start
0 likes · 15 min read
Overview of Recommendation Systems: Goals, Methods, Architecture, and Practical Considerations
Baidu Intelligent Testing
Baidu Intelligent Testing
Sep 6, 2017 · Product Management

Understanding User Satisfaction Models and How to Build Them

The article explains what a user satisfaction model is, why it matters for product evaluation, and outlines a step‑by‑step methodology—including defining dimensions, collecting questionnaire data, applying statistical techniques, creating a two‑dimensional evaluation matrix, and deriving actionable improvement plans—to quantitatively assess and enhance user experience.

EvaluationUX Researchmodeling
0 likes · 6 min read
Understanding User Satisfaction Models and How to Build Them
21CTO
21CTO
Mar 18, 2016 · Artificial Intelligence

10 Essential Tips for Building High‑Performance Intelligent Recommendation Systems

This article outlines ten practical key points—including leveraging explicit and implicit feedback, hybridizing algorithms, handling temporal and geographic factors, exploiting social ties, solving cold‑start issues, optimizing presentation, defining clear metrics, ensuring real‑time updates, and scaling big‑data processing—to help engineers design effective intelligent recommendation systems.

Evaluationcold-startdata mining
0 likes · 18 min read
10 Essential Tips for Building High‑Performance Intelligent Recommendation Systems