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iQIYI Technical Product Team
iQIYI Technical Product Team
Oct 18, 2019 · Artificial Intelligence

iQIYI Effect Advertising: Algorithm Architecture, Click‑Conversion Estimation, and Smart Bidding

The talk details iQIYI’s effect advertising system, describing its feed and in‑frame architecture, the oCPX billing model, multi‑stage recall‑ranking pipelines, real‑time feature engineering, online FM and Wide&Deep models for sparse conversion prediction, and a smart‑bidding mechanism that balances cost, quality, and volume.

Online Learningadvertising algorithmfeature engineering
0 likes · 11 min read
iQIYI Effect Advertising: Algorithm Architecture, Click‑Conversion Estimation, and Smart Bidding
DataFunTalk
DataFunTalk
Oct 17, 2019 · Artificial Intelligence

iQIYI Effect Advertising: Architecture, Click & Conversion Rate Estimation, and Intelligent Bidding

This article presents iQIYI's effect advertising system, detailing its dual‑engine resource slots, oCPX billing model, algorithmic challenges of high‑dimensional sparse conversion data, the personalized recommendation pipeline, feature engineering across real‑time, short‑term and long‑term signals, and the intelligent bidding mechanism that balances cost control with traffic quality.

AdvertisingMachine Learningclick-through rate
0 likes · 9 min read
iQIYI Effect Advertising: Architecture, Click & Conversion Rate Estimation, and Intelligent Bidding
58 Tech
58 Tech
Oct 12, 2019 · Artificial Intelligence

Recruitment Recommendation System: Ranking Framework, Model Evolution, and Feature Engineering

This article details 58.com’s recruitment recommendation platform, describing its personalized matching challenges, typical recommendation scenarios, a three‑stage ranking framework, optimization goals, the evolution from rule‑based methods to logistic regression, factorization machines, XGBoost, and deep learning models, extensive feature engineering practices, and future research directions.

AIMachine Learningdeep learning
0 likes · 16 min read
Recruitment Recommendation System: Ranking Framework, Model Evolution, and Feature Engineering
DataFunTalk
DataFunTalk
Sep 11, 2019 · Artificial Intelligence

AutoML for Tabular Data: Research, Techniques, and Applications

This talk presents the research and practical deployment of AutoML for tabular data, covering background, automated feature engineering and selection, hyper‑parameter optimization, the AutoCross feature‑crossing system, case studies, and future directions, demonstrating its advantages over Google Cloud AutoML on multiple Kaggle competitions.

AutoMLfeature engineeringhyperparameter optimization
0 likes · 14 min read
AutoML for Tabular Data: Research, Techniques, and Applications
Architecture Digest
Architecture Digest
Sep 9, 2019 · Artificial Intelligence

Overview of Recommendation System Architecture, Algorithms, and Evaluation

This article provides a comprehensive introduction to recommendation systems, covering their definition, overall offline and online architectures, feature engineering, collaborative filtering, latent semantic models, ranking algorithms, and evaluation methods including A/B testing and offline metrics.

A/B testingMachine Learningcollaborative filtering
0 likes · 28 min read
Overview of Recommendation System Architecture, Algorithms, and Evaluation
Alibaba Cloud Developer
Alibaba Cloud Developer
Sep 3, 2019 · Artificial Intelligence

Unlocking Scalable Private‑Domain Recommendations with a “4+N” Architecture

This article describes a systematic, standardized, and automated “4+N” recommendation framework that unifies features, samples, models, and pipelines to accelerate private‑domain marketing recommendations across multiple scenarios while improving accuracy, efficiency, and business impact.

AI ArchitectureModel Deploymentdeep learning
0 likes · 12 min read
Unlocking Scalable Private‑Domain Recommendations with a “4+N” Architecture
Amap Tech
Amap Tech
Aug 27, 2019 · Artificial Intelligence

POI Category Tagging: Multi‑Label Classification, Feature Engineering and Model Design

The system tackles POI category tagging as a multi‑label classification problem by engineering textual and non‑textual features, mining click‑log and external samples through active learning, and deploying hierarchical and per‑tag deep textCNN models with feature fusion, achieving over 5 % accuracy gain, ten‑fold speedup, and markedly higher precision and coverage that boost map‑search relevance.

Machine LearningPOI taggingTextCNN
0 likes · 19 min read
POI Category Tagging: Multi‑Label Classification, Feature Engineering and Model Design
Amap Tech
Amap Tech
Aug 6, 2019 · Artificial Intelligence

Boosting ETA Accuracy: How TCN Improves Historical Speed Prediction for Navigation

To enhance estimated arrival times in navigation, this article analyzes the shortcomings of traditional historical average methods and proposes a machine‑learning solution using Temporal Convolutional Networks combined with dynamic and static feature engineering, demonstrating reduced bad‑case rates and better handling of seasonal patterns.

ETA predictionMachine LearningTCN
0 likes · 11 min read
Boosting ETA Accuracy: How TCN Improves Historical Speed Prediction for Navigation
G7 EasyFlow Tech Circle
G7 EasyFlow Tech Circle
Jul 23, 2019 · Artificial Intelligence

How Intelligent Loading/Unloading Point Detection Boosts Logistics Efficiency

This article explains how an intelligent algorithm identifies the exact start and end points of vehicle loading and unloading actions using specialized acceleration features, improving platform utilization, dispatch accuracy, and overall logistics performance while achieving over 95% detection accuracy.

Artificial IntelligenceLogisticsfeature engineering
0 likes · 9 min read
How Intelligent Loading/Unloading Point Detection Boosts Logistics Efficiency
58 Tech
58 Tech
Jul 2, 2019 · Artificial Intelligence

Magic Mirror: A Visual Data‑Intelligence Platform for Low‑Code Machine Learning

Magic Mirror is a big‑data‑based visual analytics platform that lowers the barrier of machine‑learning for non‑experts while accelerating expert workflows through visual UI, modular algorithms, distributed feature generation, and automated binary‑classification modeling.

Automated modelingBig DataMachine Learning
0 likes · 9 min read
Magic Mirror: A Visual Data‑Intelligence Platform for Low‑Code Machine Learning
DataFunTalk
DataFunTalk
Jun 25, 2019 · Artificial Intelligence

Applying AutoML to Recommendation Systems: Techniques, Optimizations, and Practical Insights

This article presents a comprehensive overview of applying Automated Machine Learning (AutoML) to recommendation systems, detailing methods for data preprocessing, feature engineering, model selection, hyper‑parameter optimization, and neural architecture search, and shares practical experiences and performance gains observed in real‑world deployments.

AutoMLfeature engineeringhyperparameter optimization
0 likes · 28 min read
Applying AutoML to Recommendation Systems: Techniques, Optimizations, and Practical Insights
Amap Tech
Amap Tech
Jun 21, 2019 · Artificial Intelligence

Improving Origin Snap Accuracy in Amap Navigation Using Machine Learning

To overcome the limitations of handcrafted rules for binding users’ reported start locations to the correct road segment, Amap built a data‑driven, list‑wise learning‑to‑rank model that leverages real‑travel and planning data, achieving a 10 % error reduction and 40 % accuracy gain on difficult origin‑snapping cases.

Machine LearningMap Navigationfeature engineering
0 likes · 10 min read
Improving Origin Snap Accuracy in Amap Navigation Using Machine Learning
Sohu Tech Products
Sohu Tech Products
Apr 17, 2019 · Artificial Intelligence

CTR Estimation in Recommendation Systems: From Logistic Regression to Deep & Cross Networks

This article reviews the evolution of click‑through‑rate (CTR) estimation models for recommendation ranking, covering logistic regression, feature‑engineering tricks, factorization machines, deep neural networks, wide‑and‑deep architectures, and the Deep & Cross Network, while discussing their strengths, limitations, and future research directions.

CTRRecommendation Systemscross network
0 likes · 14 min read
CTR Estimation in Recommendation Systems: From Logistic Regression to Deep & Cross Networks
Meituan Technology Team
Meituan Technology Team
Jan 17, 2019 · Artificial Intelligence

Evolution of Meituan-Dianping Search Core Ranking: From Traditional Models to LambdaDNN Listwise Deep Learning

The Meituan‑Dianping search team progressed its core ranking from linear, FM and GBDT models to a knowledge‑graph‑enhanced deep‑learning architecture, culminating in the listwise LambdaDNN network that directly optimizes NDCG, supported by extensive feature engineering, distributed TensorFlow training, and the Athena diagnostic system.

LambdaDNNLearning-to-Rankdeep learning
0 likes · 29 min read
Evolution of Meituan-Dianping Search Core Ranking: From Traditional Models to LambdaDNN Listwise Deep Learning
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 SafetyReal-time Trainingevaluation
0 likes · 18 min read
Inside Toutiao’s Recommendation Engine: Architecture, Features, and Evaluation
58 Tech
58 Tech
Jan 11, 2019 · Artificial Intelligence

Design and Implementation of an End-to-End Efficiency Optimization Platform for 58.com Classified Listings

This article describes the design and implementation of a comprehensive efficiency‑optimization platform at 58.com, detailing its end‑to‑end workflow—from log aggregation and feature extraction through machine learning model training and online experimentation—highlighting modular, configurable, and scalable solutions for multi‑business, multi‑product ranking.

Machine Learningclick-through rateconversion rate
0 likes · 25 min read
Design and Implementation of an End-to-End Efficiency Optimization Platform for 58.com Classified Listings
JD Tech Talk
JD Tech Talk
Jan 10, 2019 · Artificial Intelligence

Sensitive Field Identification Using Wide & Deep and TextCNN Models

This article presents a machine‑learning approach for detecting sensitive data fields in a data warehouse by combining a Wide & Deep network with a TextCNN architecture, detailing data exploration, model design, training strategies, performance results, and deployment workflow.

Data GovernanceTextCNNWide&Deep
0 likes · 9 min read
Sensitive Field Identification Using Wide & Deep and TextCNN Models
21CTO
21CTO
Dec 25, 2018 · Artificial Intelligence

Demystifying Learning to Rank: From Core Concepts to Scalable Online Architecture

This article offers a comprehensive, system‑engineer‑focused guide to Learning to Rank, covering fundamental machine‑learning concepts, evaluation metrics, training approaches, and a detailed online ranking architecture with feature, recall, and model governance, illustrated by real‑world examples from Meituan‑Dianping.

A/B testingLearning-to-RankModel Deployment
0 likes · 32 min read
Demystifying Learning to Rank: From Core Concepts to Scalable Online Architecture
Meituan Technology Team
Meituan Technology Team
Dec 20, 2018 · Artificial Intelligence

Demystifying Learning to Rank: From Core Algorithms to Scalable Online Sorting Architecture

This article provides a comprehensive, system‑engineer‑focused guide to Learning to Rank, covering fundamental machine‑learning concepts, evaluation metrics such as Precision, nDCG and ERR, training‑testing‑inference stages, pointwise/pairwise/listwise methods, and a detailed multi‑layer online ranking architecture with feature, model and recall governance.

A/B testingDomain‑Driven DesignLearning-to-Rank
0 likes · 29 min read
Demystifying Learning to Rank: From Core Algorithms to Scalable Online Sorting Architecture
Alibaba Cloud Developer
Alibaba Cloud Developer
Dec 4, 2018 · Artificial Intelligence

Unlocking Elastic TensorFlow: Boosting Online Recommendation CTR by 30%

This article presents a comprehensive set of innovations—including elastic feature scaling, a Group Lasso optimizer, streaming frequency filtering, and graph‑cut model compression—that transform TensorFlow for large‑scale online learning, delivering significant CTR gains and up to 90% model size reduction in Alibaba's recommendation systems.

Model CompressionOnline LearningRecommendation Systems
0 likes · 19 min read
Unlocking Elastic TensorFlow: Boosting Online Recommendation CTR by 30%
58 Tech
58 Tech
Nov 9, 2018 · Artificial Intelligence

Search List Ranking Efficiency Optimization Practices at 58.com

This article details how 58.com improved the efficiency of its search list ranking by moving from simple time‑based ordering to a comprehensive ranking framework that incorporates feedback strategies, basic machine‑learning models, feature upgrades, and advanced model upgrades, achieving significant gains in click‑through, conversion, and revenue across multiple business lines.

Machine Learningclick-through ratefeature engineering
0 likes · 23 min read
Search List Ranking Efficiency Optimization Practices at 58.com
UC Tech Team
UC Tech Team
Nov 5, 2018 · Artificial Intelligence

News Page Identification Using Machine Learning: Feature Engineering, Model Selection, and Evaluation

To accurately distinguish news pages from other web page types, this study formulates the task as a binary classification problem, extracts 19 engineered features from HTML, evaluates logistic regression and SVM models with cross‑validation, and achieves over 90% precision, recall, and F1‑score using LR with Newton method.

Machine LearningWeb Crawlingbinary classification
0 likes · 13 min read
News Page Identification Using Machine Learning: Feature Engineering, Model Selection, and Evaluation
58 Tech
58 Tech
Oct 31, 2018 · Artificial Intelligence

Overview of the WPAI AI Platform Architecture and Implementation

The article presents a comprehensive overview of the WPAI (Wuba Platform of AI) architecture, detailing its machine‑learning and deep‑learning components, feature‑engineering framework, distributed training pipelines, online prediction services, and deployment on Kubernetes‑managed GPU/CPU resources to accelerate AI applications across 58.com business lines.

AI PlatformMachine LearningWPAI
0 likes · 15 min read
Overview of the WPAI AI Platform Architecture and Implementation
DataFunTalk
DataFunTalk
Oct 26, 2018 · Artificial Intelligence

Large‑Scale Machine Learning and AutoML Techniques for Search Advertising CTR Prediction

The article explains how large‑scale machine learning and AutoML are applied to search advertising click‑through‑rate (CTR) prediction, covering problem definition, feature generation, model training, optimization methods, distributed systems, and recent advances in AutoML with practical case studies.

AutoMLCTR predictionLarge-scale ML
0 likes · 15 min read
Large‑Scale Machine Learning and AutoML Techniques for Search Advertising CTR Prediction
MaGe Linux Operations
MaGe Linux Operations
Sep 21, 2018 · Artificial Intelligence

What Classic Diagrams Reveal About Test Error, Overfitting, and Model Selection

The article presents a series of insightful diagrams that illustrate core machine‑learning concepts such as the relationship between training and test error, the dangers of under‑ and over‑fitting, Occam’s razor, feature interactions, discriminative versus generative models, loss functions, least‑squares geometry, and sparsity.

Loss FunctionsMachine LearningModel selection
0 likes · 6 min read
What Classic Diagrams Reveal About Test Error, Overfitting, and Model Selection
Qizhuo Club
Qizhuo Club
Sep 11, 2018 · Artificial Intelligence

How 360 Mobile Assistant Built a Scalable AI‑Powered App Recommendation System

This article details the design, architecture, and key components of 360 Mobile Assistant's recommendation system, covering business scenarios, data warehouse and computing layers, feature engineering, model selection, and online deployment strategies to improve app discovery and user engagement.

CTR predictionData WarehouseMachine Learning
0 likes · 19 min read
How 360 Mobile Assistant Built a Scalable AI‑Powered App Recommendation System
360 Tech Engineering
360 Tech Engineering
Aug 22, 2018 · Artificial Intelligence

Rules of Machine Learning: 43 Practical Guidelines for Building Robust ML Systems

This article translates and summarizes Martin Zinkevich’s “Rules of ML”, offering 43 concise, experience‑based recommendations that cover terminology, pipeline design, feature engineering, monitoring, training‑serving consistency, and model iteration to help engineers build reliable machine‑learning‑driven products.

Best PracticesML pipelineMachine Learning
0 likes · 35 min read
Rules of Machine Learning: 43 Practical Guidelines for Building Robust ML Systems
Qizhuo Club
Qizhuo Club
Aug 17, 2018 · Artificial Intelligence

43 Essential Rules for Building Robust Machine Learning Systems

These 43 practical rules, adapted from Martin Zinkevich’s “Rules of ML,” guide engineers through terminology, pipeline design, feature engineering, monitoring, and model deployment, offering actionable advice to avoid common pitfalls and build reliable, scalable machine‑learning‑driven products.

Best PracticesEngineeringMachine Learning
0 likes · 41 min read
43 Essential Rules for Building Robust Machine Learning Systems
Architecture Digest
Architecture Digest
Jul 29, 2018 · Artificial Intelligence

Design and Implementation of a Machine Learning Data Platform at Getui

This article describes Getui's end‑to‑end machine‑learning data platform, covering business use cases, the full ML workflow from data ingestion and feature engineering to model training, deployment, monitoring, and the practical tools and solutions adopted to address common challenges in large‑scale AI projects.

AIData PlatformJupyter
0 likes · 11 min read
Design and Implementation of a Machine Learning Data Platform at Getui
Qunar Tech Salon
Qunar Tech Salon
Jul 10, 2018 · Artificial Intelligence

Design and Implementation of Qunar's Algorithm Service Platform for Machine Learning

The article describes the background, design, key components, and current status of Qunar's algorithm service platform, which provides a unified, scalable, and automated environment for feature engineering, model training, deployment, monitoring, and management of machine‑learning projects within the company's large‑accommodation division.

Machine LearningModel ManagementMonitoring
0 likes · 15 min read
Design and Implementation of Qunar's Algorithm Service Platform for Machine Learning
Meitu Technology
Meitu Technology
Jun 25, 2018 · Artificial Intelligence

Meitu's Personalized Recommendation System: Architecture, Features, and Optimization Strategies

Meitu’s personalized recommendation platform for the Meipai app combines offline feature engineering, near‑real‑time streaming, and online serving to recall, estimate, and rank billions of short videos using multi‑modal content features, user profiling, online learning, cold‑start bandit strategies, and multi‑objective diversity optimization, delivering timely, diverse feeds across live, homepage, and video‑detail scenarios.

Machine LearningOnline Learningcold start
0 likes · 17 min read
Meitu's Personalized Recommendation System: Architecture, Features, and Optimization Strategies
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 22, 2018 · Artificial Intelligence

Essential Machine Learning Algorithms Every Beginner Must Know

This beginner-friendly guide walks through core machine‑learning concepts—from data organization and feature design to supervised and unsupervised algorithms such as perceptron, logistic regression, decision trees, LDA, and ensemble techniques—while explaining model evaluation, overfitting, and practical tuning strategies.

Machine LearningUnsupervised Learningdeep learning
0 likes · 8 min read
Essential Machine Learning Algorithms Every Beginner Must Know
JD Retail Technology
JD Retail Technology
Jun 13, 2018 · Artificial Intelligence

IJCAI 2018 International Advertising Algorithm Competition Champion Uses Transfer Learning and LightGBM for Ad Conversion Prediction

The IJCAI 2018 International Advertising Algorithm Competition was won by JD.com algorithm engineer Hua Zhixiang, who employed a two‑stage LightGBM model with transfer learning and carefully designed statistical, temporal, ranking, and representation features to achieve top conversion‑rate predictions on massive e‑commerce advertising data.

AdvertisingIJCAILightGBM
0 likes · 5 min read
IJCAI 2018 International Advertising Algorithm Competition Champion Uses Transfer Learning and LightGBM for Ad Conversion Prediction
Tencent Advertising Technology
Tencent Advertising Technology
Jun 4, 2018 · Artificial Intelligence

Tencent Advertising Algorithm Competition: FFM Approach and Feature Engineering by the Wenqiang Ge Team

The Wenqiang Ge team, winners of the first week of the Tencent Advertising Algorithm Competition rematch, detail their FFM-based solution, including baseline adoption, feature engineering with discretized continuous values, cross‑feature handling, and tool choices such as Feather storage and the xlearn library for fast training.

Ensemble ModelingFFMFeather
0 likes · 4 min read
Tencent Advertising Algorithm Competition: FFM Approach and Feature Engineering by the Wenqiang Ge Team
JD Retail Technology
JD Retail Technology
May 30, 2018 · Artificial Intelligence

Quick Q&A: Insights from JD JDATA Algorithm Competition

This article presents a rapid Q&A session with JD data scientists and architects, covering the benefits of algorithm contests for students, the unique advantages of the JDATA competition, scoring formulas, ways to improve results, strong feature extraction, real‑time modeling, algorithm selection, and the value of the competition’s special offer for future employment.

Real-time Processingalgorithm competitiondata-science
0 likes · 8 min read
Quick Q&A: Insights from JD JDATA Algorithm Competition
Tencent Advertising Technology
Tencent Advertising Technology
May 14, 2018 · Artificial Intelligence

Tencent Advertising Algorithm Competition Weekly Champion Shares Data Processing, Feature Engineering, Model Training, and Optimization Strategies

The Nanjing University team '每天队员都想改一次名字' shares their winning approach in Tencent Advertising Algorithm Competition, covering data processing, feature engineering, model training techniques, and alternative optimization targets for AUC improvement, and discuss lessons learned from previous year's champion experience.

AUC optimizationMachine LearningTencent Advertising
0 likes · 5 min read
Tencent Advertising Algorithm Competition Weekly Champion Shares Data Processing, Feature Engineering, Model Training, and Optimization Strategies
Tencent Cloud Developer
Tencent Cloud Developer
May 9, 2018 · Artificial Intelligence

From Mathematics to Machine Learning: A Personal Journey Through Recommendation, Security, and AIOps

A mathematician‑turned‑engineer recounts his 2015‑2022 path from undocumented recommendation systems at Tencent, through high‑precision security models, reinforcement‑learning game AI, quantum‑ML studies, to large‑scale AIOps time‑series anomaly detection, offering practical lessons for anyone transitioning into machine learning.

Anomaly DetectionRecommendation SystemsSQL
0 likes · 16 min read
From Mathematics to Machine Learning: A Personal Journey Through Recommendation, Security, and AIOps
Tencent Advertising Technology
Tencent Advertising Technology
May 7, 2018 · Artificial Intelligence

Choosing Mainstream CTR Models: LightGBM, FFM, and Deep Learning Approaches

The author, a graduate student and weekly champion of the Tencent advertising algorithm contest, shares practical guidance on selecting mainstream CTR models—including LightGBM, field‑aware factorization machines, and deep learning approaches—while offering tips on feature handling, hyper‑parameter settings, and resource‑efficient implementation.

CTRFFMLightGBM
0 likes · 5 min read
Choosing Mainstream CTR Models: LightGBM, FFM, and Deep Learning Approaches
Efficient Ops
Efficient Ops
Apr 17, 2018 · Artificial Intelligence

From Math to ML: My Path Through Recommendation, Security, and AIOps

This article chronicles the author’s transition from a mathematics background to machine learning, detailing early challenges, hands‑on projects in recommendation systems, security, and AIOps, and sharing practical insights on feature engineering, model evaluation, and large‑scale anomaly detection.

Anomaly DetectionMachine LearningRecommendation Systems
0 likes · 17 min read
From Math to ML: My Path Through Recommendation, Security, and AIOps
Meituan Technology Team
Meituan Technology Team
Mar 29, 2018 · Artificial Intelligence

Deep Learning Model Applications and Optimizations for Recommendation Ranking at Meituan

The paper describes how Meituan tackles information overload on its lifestyle platform by training multi‑task deep neural networks on billions of interaction logs using a distributed PS‑Lite framework, employing sophisticated feature engineering, missing‑value imputation, KL‑regularization and Neural Factorization Machines to boost offline AUC and online CTR in the “Guess You Like” recommendation feed, while introducing training‑time optimizations and outlining future multi‑task and contextual enhancements.

Recommendation Systemsdeep learningfeature engineering
0 likes · 16 min read
Deep Learning Model Applications and Optimizations for Recommendation Ranking at Meituan
Baobao Algorithm Notes
Baobao Algorithm Notes
Mar 28, 2018 · Artificial Intelligence

Mastering CTR/CVR Prediction: Core Techniques and Resources from Recent Competitions

This article reviews the fundamentals of click‑through‑rate (CTR) and conversion‑rate (CVR) prediction, explains why the problem is challenging due to high‑dimensional sparse features, and summarizes classic and modern modeling approaches—including feature engineering, linear models, factorization machines, GBDT‑LR, and deep neural networks—while providing practical code snippets and useful research links.

CTRCVRclick-through rate
0 likes · 8 min read
Mastering CTR/CVR Prediction: Core Techniques and Resources from Recent Competitions
Hulu Beijing
Hulu Beijing
Jan 23, 2018 · Artificial Intelligence

Feature Engineering for Structured Data: Normalization, Encoding & Interaction

This article explains the fundamentals of feature engineering for structured data, covering why and how to normalize numerical features, various categorical encoding techniques, methods for creating high‑dimensional interaction features, and decision‑tree based strategies for efficiently discovering valuable feature combinations.

categorical encodingfeature engineeringinteraction features
0 likes · 12 min read
Feature Engineering for Structured Data: Normalization, Encoding & Interaction
Hulu Beijing
Hulu Beijing
Jan 18, 2018 · Artificial Intelligence

Why Accuracy Misleads and How to Pick Better ML Evaluation Metrics

This article uses realistic Hulu business scenarios to illustrate the pitfalls of relying solely on accuracy, precision, recall, RMSE, and other single metrics, and explains how combining complementary evaluation measures such as average accuracy, precision‑recall curves, ROC, F1‑score, and MAPE can provide a more comprehensive assessment of classification, ranking, and regression models.

RMSEaccuracyfeature engineering
0 likes · 12 min read
Why Accuracy Misleads and How to Pick Better ML Evaluation Metrics
Tongcheng Travel Technology Center
Tongcheng Travel Technology Center
Jan 18, 2018 · Artificial Intelligence

Tourism Spot Recommendation System: Framework, Model Construction, Feature Engineering, and Performance Evaluation

This article describes a tourism recommendation system that addresses data sparsity, seasonality, and geographic variations by using an offline‑online architecture, GBDT+LR CTR prediction, exponential decay scoring, and extensive feature engineering, achieving a 1.6% conversion‑rate increase and high accuracy and recall.

CTR predictionGBDTTourism
0 likes · 14 min read
Tourism Spot Recommendation System: Framework, Model Construction, Feature Engineering, and Performance Evaluation
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 SafetyMachine LearningReal-time Training
0 likes · 18 min read
Inside Toutiao’s Recommendation Engine: Architecture, Features, and Evaluation
iQIYI Technical Product Team
iQIYI Technical Product Team
Dec 15, 2017 · Artificial Intelligence

Sentiment Classification of iQIYI User Comments: Model Selection, Feature Engineering, and Online Deployment

The team built a lightweight three‑class sentiment classifier for iQIYI user comments using a linear‑kernel SVM with high‑dimensional bag‑of‑words features and an expanded ~100k word lexicon, achieving over 96% accuracy across domains, and deployed it as a Spring Boot PMML service with zero‑downtime refresh, while planning GBDT‑enhanced features and word‑embedding optimizations.

DeploymentNLPSentiment Analysis
0 likes · 13 min read
Sentiment Classification of iQIYI User Comments: Model Selection, Feature Engineering, and Online Deployment
Qunar Tech Salon
Qunar Tech Salon
Dec 5, 2017 · Information Security

Machine Learning Practices for Web Attack Detection at Ctrip

This article describes Ctrip’s evolution from rule‑based web attack detection to a Spark‑powered machine‑learning system, detailing the Nile architecture, data collection, feature engineering with TF‑IDF, model training, evaluation metrics, online deployment, and future enhancements for information security.

Machine LearningWeb Securityattack detection
0 likes · 17 min read
Machine Learning Practices for Web Attack Detection at Ctrip
Meituan Technology Team
Meituan Technology Team
Sep 21, 2017 · Big Data

Feature Production Scheduling: Architecture Evolution and Core Technologies

Using Meituan‑Dianping’s hospitality online feature system as a case study, the article describes how feature production scheduling evolved from offline batch ETL to automated, metadata‑driven pipelines and sub‑second streaming, detailing the underlying architecture, incremental updates, storage abstraction, write‑shaving, atomicity, and recovery mechanisms.

Big DataReal-time ProcessingSystem Architecture
0 likes · 23 min read
Feature Production Scheduling: Architecture Evolution and Core Technologies
21CTO
21CTO
Sep 20, 2017 · Big Data

Winning O2O Coupon Redemption with XGBoost, GBDT, and Feature Engineering

This article details a data-driven solution for the 2016 O2O coupon redemption competition, describing dataset partitioning, extensive feature engineering across user, merchant, and coupon dimensions, handling leakage, and model fusion using XGBoost, GBDT, and RandomForest, achieving top AUC scores through weighted ensemble.

GBDTXGBoostcoupon redemption
0 likes · 12 min read
Winning O2O Coupon Redemption with XGBoost, GBDT, and Feature Engineering
Qunar Tech Salon
Qunar Tech Salon
Aug 16, 2017 · Artificial Intelligence

Applying Wide & Deep Learning to Meituan‑Dianping Recommendation System

This article describes how Meituan‑Dianping leverages deep learning, especially the Wide & Deep model, to improve its recommendation system by addressing business diversity, user context, feature engineering challenges, optimizer and loss function choices, and presents offline and online experimental results showing significant CTR gains.

CTRMachine LearningWide&Deep
0 likes · 22 min read
Applying Wide & Deep Learning to Meituan‑Dianping Recommendation System
Baidu Waimai Technology Team
Baidu Waimai Technology Team
Jun 27, 2017 · Artificial Intelligence

Detecting Low‑Quality New Users in Food Delivery with a GBDT + LR Model

The article describes a data‑driven approach for identifying low‑value new users in a food‑delivery platform by labeling 7‑day repeat‑purchase behavior, extracting order, behavior, merchant and user features, and training a combined Gradient Boosted Decision Tree and Logistic Regression model to improve fraud detection and merchant penalty decisions.

AIGBDTfeature engineering
0 likes · 7 min read
Detecting Low‑Quality New Users in Food Delivery with a GBDT + LR Model
Tencent Advertising Technology
Tencent Advertising Technology
Jun 25, 2017 · Artificial Intelligence

Interview with ‘拔萝卜’: Lessons Learned from the Tencent Social Ads Algorithm Competition

In this interview, a solo female participant from Shanghai Jiao Tong University shares her experience, challenges, and technical insights—including feature engineering, memory management, and model tuning with XGBoost and LightGBM—gained while competing in the Tencent Social Ads algorithm contest.

Machine LearningModel tuningTencent
0 likes · 5 min read
Interview with ‘拔萝卜’: Lessons Learned from the Tencent Social Ads Algorithm Competition
Qunar Tech Salon
Qunar Tech Salon
May 15, 2017 · Artificial Intelligence

Building an Algorithm Platform for Machine Learning Deployment at Qunar

The article describes how a three‑stage algorithm platform was designed and implemented to automate model deployment, unify feature processing, and provide service‑oriented model evaluation, debugging, and monitoring for machine‑learning applications in a large e‑commerce environment.

AI servicesAlgorithm PlatformJava
0 likes · 10 min read
Building an Algorithm Platform for Machine Learning Deployment at Qunar
Tencent Advertising Technology
Tencent Advertising Technology
May 9, 2017 · Artificial Intelligence

Kaggle Competition Overview and Practical Guide for Data Mining

This article provides a comprehensive introduction to Kaggle, covering its history, competition formats, participation rules, public and private leaderboard mechanics, and a step‑by‑step workflow that includes data analysis, cleaning, feature engineering, model training, validation, hyper‑parameter tuning, ensemble techniques, and automation frameworks for successful data‑mining contests.

KaggleMachine Learningfeature engineering
0 likes · 24 min read
Kaggle Competition Overview and Practical Guide for Data Mining
Architecture Digest
Architecture Digest
Apr 9, 2017 · Artificial Intelligence

Migrating Youku Tudou Video Recommendation System from Offline to Online Sorting

The article details how Youku Tudou redesigned its video recommendation architecture, moving ranking from offline to online processing, outlining the comparative architecture, benefits, challenges, feature handling, offline evaluation methods, and weight‑fusion techniques that enabled a successful launch after two months of development.

AB testingAUC evaluationfeature engineering
0 likes · 7 min read
Migrating Youku Tudou Video Recommendation System from Offline to Online Sorting
21CTO
21CTO
Mar 22, 2017 · Artificial Intelligence

How Youku Tudou Revamped Its Video Recommendation Engine for Real‑Time Ranking

The Youku Tudou data team overhauled its video recommendation system by moving ranking from offline to online, detailing architectural changes, advantages, challenges, feature handling, offline evaluation, and model weight fusion to improve scalability and user experience.

AB testingAISystem Architecture
0 likes · 7 min read
How Youku Tudou Revamped Its Video Recommendation Engine for Real‑Time Ranking
21CTO
21CTO
Mar 2, 2017 · Artificial Intelligence

How User Personas Power Modern Recommendation Systems: From Theory to NetEase Yanxuan

This article explains the concept and construction of user personas, explores the essence and algorithms of recommendation systems, compares movie and e‑commerce scenarios, and details NetEase Yanxuan's practical CTR‑based recommendation model with extensive feature engineering.

Machine LearningRecommendation Systemse-commerce
0 likes · 13 min read
How User Personas Power Modern Recommendation Systems: From Theory to NetEase Yanxuan
Meituan Technology Team
Meituan Technology Team
Mar 2, 2017 · Big Data

Meituan Waimai Feature Archive Platform: Architecture, Tag System, and Data Processing

Meituan Waimai’s Feature Archive platform processes billions of daily orders by managing ~200 user and 400 merchant tags through a three‑layer architecture—Hive, Elasticsearch, HBase, and MySQL—offering visual tag selection, instant self‑service queries, full data extraction, and a predicate‑logic query language, while supporting future extensibility.

Big DataElasticsearchHBase
0 likes · 14 min read
Meituan Waimai Feature Archive Platform: Architecture, Tag System, and Data Processing
Alibaba Cloud Developer
Alibaba Cloud Developer
Feb 27, 2017 · Artificial Intelligence

Essential Machine Learning Algorithms Every Beginner Must Know

This guide introduces beginners to core machine learning concepts, covering feature design, supervised and unsupervised methods such as perceptron, logistic regression, decision trees, LDA, and ensemble techniques like bagging and boosting, while explaining model evaluation, overfitting, and practical optimization strategies.

Machine LearningUnsupervised Learningensemble methods
0 likes · 9 min read
Essential Machine Learning Algorithms Every Beginner Must Know
Meituan Technology Team
Meituan Technology Team
Dec 9, 2016 · Artificial Intelligence

A General Feature Production Framework for Meituan Delivery Ranking System

The paper presents a generic feature‑production framework for Meituan’s food‑delivery ranking system that abstracts statistical feature generation, storage, retrieval, and online loading into configurable dimensions, metrics and operators, enabling developers to add new features with minimal code and dramatically speeding up machine‑learning model iteration.

KV StoreMachine Learningfeature engineering
0 likes · 12 min read
A General Feature Production Framework for Meituan Delivery Ranking System
StarRing Big Data Open Lab
StarRing Big Data Open Lab
Nov 4, 2016 · Artificial Intelligence

How Item Features Power Music Recommendations: A Hands‑On Guide

This article explains how recommendation systems can use item‑level features instead of user ratings, illustrating the approach with Pandora's music‑gene project, detailing feature selection, scoring, distance calculations, standardization, and classification techniques across music, athlete, Iris, and automobile datasets.

Machine LearningRecommendation Systemsclassification
0 likes · 20 min read
How Item Features Power Music Recommendations: A Hands‑On Guide
Alibaba Cloud Developer
Alibaba Cloud Developer
Oct 8, 2016 · Artificial Intelligence

Unlocking Machine Learning Basics: From Perceptrons to Ensemble Models

An introductory guide for machine‑learning beginners that covers essential algorithms—including perceptrons, logistic regression, decision trees, LDA, and ensemble techniques like bagging and boosting—explains feature design, model training, evaluation, and practical tips for avoiding under‑ and over‑fitting.

Decision TreesMachine LearningUnsupervised Learning
0 likes · 8 min read
Unlocking Machine Learning Basics: From Perceptrons to Ensemble Models
21CTO
21CTO
Sep 20, 2016 · Artificial Intelligence

What Quora’s VP Reveals About Building Real‑World Recommender Systems

In this talk, Quora’s VP of Engineering Xavier Amatriain shares practical lessons from building the company’s large‑scale recommender system, covering data richness, implicit signals, model choices, feature engineering, evaluation strategies, and why distribution isn’t always required.

feature engineeringimplicit feedbackmatrix factorization
0 likes · 4 min read
What Quora’s VP Reveals About Building Real‑World Recommender Systems
Qunar Tech Salon
Qunar Tech Salon
Aug 21, 2016 · Artificial Intelligence

Hotel Search Ranking: Problem Definition, Model Construction, Feature Engineering, and Offline Evaluation

This article presents a comprehensive overview of hotel search ranking, covering problem definition, the distinction between ranking and probability estimation, handling position bias, detailed feature engineering, the AnyBoost linear boosting model, offline evaluation methods, and observed online performance improvements.

Learning-to-RankMachine Learningfeature engineering
0 likes · 7 min read
Hotel Search Ranking: Problem Definition, Model Construction, Feature Engineering, and Offline Evaluation
Ctrip Technology
Ctrip Technology
Jul 29, 2016 · Artificial Intelligence

Applying Deep Learning to Sogou Mobile Search Advertising: Multi‑Model Fusion for CTR Prediction

This article presents how deep learning techniques are applied to Sogou's mobile search advertising, detailing the system architecture, feature design, multi‑model fusion strategies, engineering implementation, evaluation metrics, and future directions for improving CTR prediction performance.

CTR predictionModel Fusiondeep learning
0 likes · 13 min read
Applying Deep Learning to Sogou Mobile Search Advertising: Multi‑Model Fusion for CTR Prediction
Architecture Digest
Architecture Digest
Mar 29, 2016 · Artificial Intelligence

Practical Guide to Machine Learning: Problem Modeling, Data Preparation, Feature Engineering, Model Training and Optimization

This article presents a comprehensive, practical guide to applying machine learning in industry, covering problem modeling, data preparation, feature extraction, model training, and optimization, illustrated with a DEAL transaction amount forecasting case study.

Machine Learningdata preparationfeature engineering
0 likes · 17 min read
Practical Guide to Machine Learning: Problem Modeling, Data Preparation, Feature Engineering, Model Training and Optimization
21CTO
21CTO
Feb 12, 2016 · Artificial Intelligence

Can Machine Learning Reveal the True Author of Red Mansions' Final 40 Chapters?

This article uses machine learning to compare lexical patterns between the first 80 and last 40 chapters of 'Dream of the Red Chamber', demonstrating distinct stylistic differences that support the scholarly view that the final chapters were not authored by Cao Xueqin.

Machine LearningRed MansionsSupport Vector Machine
0 likes · 6 min read
Can Machine Learning Reveal the True Author of Red Mansions' Final 40 Chapters?
21CTO
21CTO
Jan 6, 2016 · Artificial Intelligence

From Naïve Algorithms to Scalable Recommendations: Jiayuan’s Journey

This article chronicles the evolution of Jiayuan’s dating recommendation system from early item‑based kNN experiments through a feature‑engineering focused engineering year and a product‑oriented optimization phase, while also reviewing several advanced machine‑learning techniques the author explored.

Machine LearningRecommendation Systemsfeature engineering
0 likes · 15 min read
From Naïve Algorithms to Scalable Recommendations: Jiayuan’s Journey
Architect
Architect
Nov 16, 2015 · Artificial Intelligence

Meituan O2O Search Ranking System: Online Architecture and Techniques

This article describes Meituan's online search ranking architecture for O2O services, covering data pipelines, feature loading, ranking service workflow, A/B testing, model choices, cold‑start handling, and position bias mitigation, all tailored for mobile‑centric personalized ranking.

A/B testingfeature engineeringonline serving
0 likes · 14 min read
Meituan O2O Search Ranking System: Online Architecture and Techniques
21CTO
21CTO
Oct 16, 2015 · Artificial Intelligence

Mastering Industrial Machine Learning: From Problem Modeling to Model Optimization

This article outlines a complete industrial machine‑learning workflow—starting with problem modeling, through data preparation, feature extraction, model training, and ending with model optimization—illustrated with a real‑world DEAL revenue‑prediction case and practical tips for handling data, features, and model selection.

Industrial ApplicationMachine Learningdata preparation
0 likes · 20 min read
Mastering Industrial Machine Learning: From Problem Modeling to Model Optimization
21CTO
21CTO
Sep 14, 2015 · Artificial Intelligence

How Airbnb Builds Machine Learning Models to Detect Fraudulent Transactions

Airbnb’s trust and safety team uses a series of machine‑learning models—starting from defining the prediction target, through data sampling and feature engineering, to evaluating precision and recall—to identify and mitigate fraud risks such as chargebacks across its global peer‑to‑peer rental platform.

AIAirbnbfeature engineering
0 likes · 7 min read
How Airbnb Builds Machine Learning Models to Detect Fraudulent Transactions
21CTO
21CTO
Aug 14, 2015 · Artificial Intelligence

How Meituan Supercharges Local Services with Advanced Recommendation and Ranking

This article details Meituan's recommendation ecosystem, covering its key products, system goals, architecture, data pipelines, algorithms, cold‑start strategies, and the extensive ranking work—including modeling, sampling, bias removal, feature engineering, interleaving, and online learning—to dramatically boost user conversion.

cold startfeature engineeringranking
0 likes · 15 min read
How Meituan Supercharges Local Services with Advanced Recommendation and Ranking
Art of Distributed System Architecture Design
Art of Distributed System Architecture Design
Aug 2, 2015 · Artificial Intelligence

Designing Machine Learning Models for Fraud Detection: Sampling, Feature Engineering, and Evaluation

This article explains how Airbnb's Trust & Safety team builds machine‑learning models to detect fraudulent behavior, covering problem definition, role‑based sampling, feature design techniques such as normalization and CP‑coding, and the trade‑offs between precision and recall in model evaluation.

AISamplingfeature engineering
0 likes · 10 min read
Designing Machine Learning Models for Fraud Detection: Sampling, Feature Engineering, and Evaluation
Meituan Technology Team
Meituan Technology Team
Feb 10, 2015 · Artificial Intelligence

Practical Guide to Machine Learning at Meituan: From Problem Modeling to Model Optimization

This guide walks through Meituan’s end‑to‑end offline ML workflow—from problem modeling and data preparation, through feature engineering and normalization, to model selection, training optimization, evaluation, and iterative improvement—emphasizing business alignment, data quality, and practical diagnostics for real‑world deployment.

Industrial Applicationfeature engineeringmodel training
0 likes · 16 min read
Practical Guide to Machine Learning at Meituan: From Problem Modeling to Model Optimization
Baidu Tech Salon
Baidu Tech Salon
Mar 21, 2014 · Artificial Intelligence

Baidu's Large-Scale Machine Learning Technology: Enabling Trillion-Feature Processing with Minute-Level Model Updates

Baidu's Big Data Machine Learning team, led by Xia Fen, unveiled a suite of five novel algorithms that together allow trillion‑scale feature processing, minute‑level model updates, and up to thousand‑fold efficiency gains in training and inference, dramatically surpassing existing solutions such as Google's billion‑feature systems.

BaiduCTR predictionLarge-Scale Machine Learning
0 likes · 6 min read
Baidu's Large-Scale Machine Learning Technology: Enabling Trillion-Feature Processing with Minute-Level Model Updates