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ITPUB
ITPUB
Jun 20, 2022 · Artificial Intelligence

Edge AI Boosts Mobile Search Ranking: Inside Meituan’s On‑Device Re‑ranking

This article details Meituan’s implementation of on‑device deep learning models for search re‑ranking, covering the motivations for edge intelligence, feature engineering, feedback sequence modeling, model architecture, deployment optimizations, experimental results, and future directions, offering practical insights for developers building large‑scale AI on mobile.

edge AIfeature engineeringmobile deep learning
0 likes · 28 min read
Edge AI Boosts Mobile Search Ranking: Inside Meituan’s On‑Device Re‑ranking
DataFunTalk
DataFunTalk
May 31, 2022 · Artificial Intelligence

Using DolphinScheduler OpenMLDB Task for End‑to‑End MLOps Workflow

This article introduces the DolphinScheduler OpenMLDB Task, explains how it integrates OpenMLDB's feature platform into DolphinScheduler workflows to create a complete MLOps pipeline, and provides a step‑by‑step demonstration using the TalkingData ad‑fraud detection dataset from Kaggle.

DolphinSchedulerMLOpsOpenMLDB
0 likes · 7 min read
Using DolphinScheduler OpenMLDB Task for End‑to‑End MLOps Workflow
NetEase LeiHuo Testing Center
NetEase LeiHuo Testing Center
May 27, 2022 · Artificial Intelligence

Multimodal Model for Game Frame Rate Prediction

This article explains how a multimodal deep learning model combines static and temporal game data to predict frame rates, helping identify performance bottlenecks and improve client smoothness through feature fusion, data pipelines, and real‑time inference in modern games.

AIDeep LearningMultimodal Learning
0 likes · 7 min read
Multimodal Model for Game Frame Rate Prediction
Architect
Architect
May 19, 2022 · Artificial Intelligence

Learning to Rank (LTR) Practice in Amap Search Suggestions: From Data Collection to Model Optimization

This article details Amap's practical experience with Learning to Rank for search suggestions, covering application scenarios, data pipeline construction, feature engineering, model training, loss‑function adjustments, and the resulting performance improvements, while also discussing challenges such as sparse features and click bias.

AmapLearning-to-RankSearch Suggestion
0 likes · 9 min read
Learning to Rank (LTR) Practice in Amap Search Suggestions: From Data Collection to Model Optimization
Alipay Experience Technology
Alipay Experience Technology
May 17, 2022 · Mobile Development

How Ant Group Built an Ultra‑Real‑Time Client Feature Center for Smarter AI

This article examines the challenges of traditional data feature acquisition and presents Ant Group’s ultra‑real‑time client feature center, detailing its architecture, data collection, streaming and script computation, backflow mechanisms, and monitoring to deliver rich, timely, and easy‑to‑use features for AI models.

Event-drivenPython VMclient-side features
0 likes · 11 min read
How Ant Group Built an Ultra‑Real‑Time Client Feature Center for Smarter AI
Tencent Cloud Developer
Tencent Cloud Developer
Apr 20, 2022 · Artificial Intelligence

Coarse Ranking in Recommendation Systems: Architecture, Models, and Optimization

Coarse ranking bridges recall and fine ranking by trimming tens of thousands of candidates to a few hundred or thousand using a three‑part framework—sample construction, ordinary and cross‑feature engineering, and evolving deep models—from rule‑based to lightweight MLPs, while employing distillation, feature crossing, pruning, quantization, and bias mitigation to balance accuracy with strict latency constraints.

Artificial IntelligenceModel Optimizationcoarse ranking
0 likes · 9 min read
Coarse Ranking in Recommendation Systems: Architecture, Models, and Optimization
Baobao Algorithm Notes
Baobao Algorithm Notes
Apr 19, 2022 · Artificial Intelligence

Understanding Nonlinearity in Machine Learning: From Logistic Regression to Neural Networks

The article explores the concept of nonlinearity in machine learning, illustrating why tasks like distinguishing cat versus dog or predicting body shape from height and weight are challenging for linear models, and discusses feature engineering, kernel tricks, and periodic activation functions as strategies to introduce nonlinearity and improve model performance.

Neural Networksfeature engineeringkernel methods
0 likes · 7 min read
Understanding Nonlinearity in Machine Learning: From Logistic Regression to Neural Networks
Snowball Engineer Team
Snowball Engineer Team
Apr 11, 2022 · Artificial Intelligence

Design and Implementation of Snowball's Model Feature Management Platform

The article presents Snowball's model feature platform, detailing its motivation, architecture, feature lifecycle management, online engine design, optimization techniques, and the resulting improvements in feature iteration speed, reuse, and system stability for recommendation and search services.

Feature ManagementModel Servingfeature engineering
0 likes · 16 min read
Design and Implementation of Snowball's Model Feature Management Platform
NetEase Yanxuan Technology Product Team
NetEase Yanxuan Technology Product Team
Mar 30, 2022 · Big Data

Data Lake Construction and Practice at NetEase Yanxuan

NetEase Yanxuan replaced its cumbersome data‑warehouse with a flexible Delta‑Lake/Iceberg data lake, creating a unified metadata layer and real‑time ingestion pipelines that cut latency from nightly batches to seconds, slashed compute and storage costs, supported diverse business scenarios and machine‑learning feature engineering, and set the stage for broader future expansion.

Data IntegrationData LakeDelta Lake
0 likes · 16 min read
Data Lake Construction and Practice at NetEase Yanxuan
Yanxuan Tech Team
Yanxuan Tech Team
Mar 29, 2022 · Big Data

How NetEase Yanxuan Built a Real‑Time Data Lake to Boost Efficiency

This article explains how NetEase Yanxuan evolved from a traditional data‑warehouse pipeline to a cloud‑native data‑lake architecture, detailing the business challenges, design choices, technology stack (Delta, Iceberg, Hudi), implementation steps, and the resulting gains in real‑time data access, cost reduction, and feature‑engineering support.

Data LakeDelta LakeHudi
0 likes · 18 min read
How NetEase Yanxuan Built a Real‑Time Data Lake to Boost Efficiency
HelloTech
HelloTech
Mar 28, 2022 · Artificial Intelligence

Algorithmic Optimization for Information Flow Advertising at Hello Travel

Hello Travel tackles information‑flow advertising challenges by using LightGBM‑based models to predict order conversion, creative performance, and pre‑bid user quality, augmenting sparse data with feature engineering and uplift techniques, while planning future fully automated delivery, richer pre‑screening, and cross‑channel reinforcement‑learning enhancements.

AdvertisingAlgorithm OptimizationLightGBM
0 likes · 18 min read
Algorithmic Optimization for Information Flow Advertising at Hello Travel
Tencent Cloud Developer
Tencent Cloud Developer
Mar 15, 2022 · Artificial Intelligence

Comprehensive Overview of Ranking Models in Recommendation Systems

The article provides a thorough guide to ranking in recommendation systems, detailing the pipeline architecture, sample handling challenges, extensive feature engineering categories, the evolution from collaborative filtering to deep and attention‑based models, and key optimization trade‑offs between memorization, generalization, and efficient user‑interest modeling.

CTR predictionDeep LearningModel Optimization
0 likes · 19 min read
Comprehensive Overview of Ranking Models in Recommendation Systems
NetEase Cloud Music Tech Team
NetEase Cloud Music Tech Team
Mar 9, 2022 · Industry Insights

How NetEase Cloud Music Built a Real‑Time Live‑Stream Recommendation System

This article details the architecture, incremental model training, feature engineering, and deployment strategies that enabled NetEase Cloud Music to achieve real‑time live‑stream recommendation, covering business background, multi‑objective modeling, real‑time feature pipelines, sample attribution, feature admission, and online performance results.

Incremental LearningIndustry InsightsModel Deployment
0 likes · 26 min read
How NetEase Cloud Music Built a Real‑Time Live‑Stream Recommendation System
58 Tech
58 Tech
Feb 24, 2022 · Artificial Intelligence

Deep Learning Ranking Model Enhancements for Recruitment Search at 58.com

This report details how the Search Recommendation team at 58.com upgraded their deep learning ranking model for recruitment by adding multi-valued and semantic vector features, integrating conversion sequences, employing feature‑crossing techniques, optimizing offline data pipelines, and planning future multi‑scene improvements to boost CTR and relevance.

AICTR predictionfeature engineering
0 likes · 18 min read
Deep Learning Ranking Model Enhancements for Recruitment Search at 58.com
Baobao Algorithm Notes
Baobao Algorithm Notes
Feb 14, 2022 · Artificial Intelligence

Mastering Feature Engineering: From AutoML Dictionaries to Business‑Driven Insights

This article presents a comprehensive, practical methodology for feature engineering that combines brute‑force AutoML‑style dictionary searches, business‑logic‑driven feature creation, and feature‑importance‑guided refinement, illustrating each approach with real Kaggle competition examples and concrete code snippets.

AutoMLKaggledata preprocessing
0 likes · 12 min read
Mastering Feature Engineering: From AutoML Dictionaries to Business‑Driven Insights
DataFunTalk
DataFunTalk
Jan 21, 2022 · Databases

Akulaku’s Adoption of OpenMLDB: Business Scenarios, Technical Architecture, and Evolution Recommendations

This article outlines Akulaku’s background, explores its business scenarios and challenges, details how OpenMLDB’s unified feature‑engineering platform and consistent technology stack address real‑time and offline data processing needs, presents performance comparisons across use cases, and offers cost‑benefit analysis and future improvement suggestions.

FinTechOpenMLDBfeature engineering
0 likes · 18 min read
Akulaku’s Adoption of OpenMLDB: Business Scenarios, Technical Architecture, and Evolution Recommendations
DataFunTalk
DataFunTalk
Jan 8, 2022 · Artificial Intelligence

Survey of Classic Recommendation Algorithms: LR, FM, FFM, WDL, DeepFM, DCN, and xDeepFM

This article surveys classic recommendation algorithms—including Logistic Regression, Factorization Machines, Field‑aware FM, Wide & Deep, DeepFM, DCN, and xDeepFM—explaining their principles, feature preprocessing, problem scopes, and industrial applications within personalized recommendation systems.

Deep Learningfactorization machinesfeature engineering
0 likes · 12 min read
Survey of Classic Recommendation Algorithms: LR, FM, FFM, WDL, DeepFM, DCN, and xDeepFM
DataFunTalk
DataFunTalk
Jan 7, 2022 · Artificial Intelligence

Building an Intelligent Risk Control Tool System: Architecture and Key Components

This article presents a comprehensive overview of constructing an intelligent risk control tool system, detailing its evolution from manual processes to automated platforms, describing the core "three‑piece" suite (model, decision, and feature platforms) along with supporting data and monitoring platforms, and explaining the functions and interactions of each module such as data ingestion, feature engineering, automated modeling, decision flow, and real‑time monitoring.

Data PlatformModelingdecision engine
0 likes · 13 min read
Building an Intelligent Risk Control Tool System: Architecture and Key Components
Baobao Algorithm Notes
Baobao Algorithm Notes
Jan 5, 2022 · Artificial Intelligence

When to Use Logistic Regression, SVM, Decision Trees, and More? A Practical Frequency Guide

This article analyzes how often common machine‑learning algorithms such as k‑NN, Naïve Bayes, decision trees, SVM, logistic regression, and neural networks are used in industry, explains their typical scenarios, highlights strengths and weaknesses, and shows how non‑linearity and feature engineering affect their suitability.

algorithm comparisondecision treefeature engineering
0 likes · 12 min read
When to Use Logistic Regression, SVM, Decision Trees, and More? A Practical Frequency Guide
Python Programming Learning Circle
Python Programming Learning Circle
Dec 21, 2021 · Artificial Intelligence

Introduction to CatBoost: Features, Advantages, and Practical Implementation

This article introduces CatBoost, outlines its key advantages such as automatic handling of categorical features, symmetric trees, and feature combination, and provides a step‑by‑step Python tutorial—including data preparation, model training, visualization, and feature importance analysis—using a CTR prediction dataset.

CatBoostModel EvaluationPython
0 likes · 5 min read
Introduction to CatBoost: Features, Advantages, and Practical Implementation
IEG Growth Platform Technology Team
IEG Growth Platform Technology Team
Dec 20, 2021 · Artificial Intelligence

Comprehensive Guide to pCTR Modeling, Optimization, and Online Learning in Real‑Time Advertising Systems

This article presents a three‑part technical guide covering the fundamentals of computational advertising and real‑time bidding, detailed offline pCTR model training pipelines with feature engineering, calibration and model structure improvements, and advanced online learning techniques such as parameter freezing, sample replay and knowledge distillation, all aimed at boosting CTR performance and reducing bias in large‑scale ad platforms.

AdvertisingCTR predictionOnline Learning
0 likes · 37 min read
Comprehensive Guide to pCTR Modeling, Optimization, and Online Learning in Real‑Time Advertising Systems
iQIYI Technical Product Team
iQIYI Technical Product Team
Nov 12, 2021 · Artificial Intelligence

iQIYI Generic Ranking Framework for Video Recommendation

iQIYI’s generic ranking framework unifies feature production, replay, training, and ranking into modular, configurable phases that handle offline and real‑time data, support diverse models, provide automated monitoring, and have been deployed across all platforms, delivering over 20% higher watch time and doubling first‑play videos.

feature engineeringonline servingranking framework
0 likes · 15 min read
iQIYI Generic Ranking Framework for Video Recommendation
ByteDance Terminal Technology
ByteDance Terminal Technology
Nov 9, 2021 · Artificial Intelligence

Edge AI Video Preloading: Case Study and Implementation with ByteDance's Client AI Platform

This article presents a comprehensive case study of applying edge AI to video preloading on the Xigua Video platform, detailing scenario analysis, predictive modeling of user behavior, feature engineering, on‑device model inference, dynamic algorithm package deployment, experimental evaluation, and the resulting performance and cost improvements.

A/B testingModel Optimizationclient inference
0 likes · 18 min read
Edge AI Video Preloading: Case Study and Implementation with ByteDance's Client AI Platform
DataFunTalk
DataFunTalk
Nov 3, 2021 · Artificial Intelligence

Deep Learning for Time‑Series Modeling in Financial Risk Management

This article describes how a financial company leveraged deep‑learning sequence models to automatically extract features from massive time‑series data, improving risk‑assessment models and operational efficiency through a unified framework that includes data preprocessing, embedding, field and item aggregation, and end‑to‑end deployment.

AIDeep LearningModeling
0 likes · 10 min read
Deep Learning for Time‑Series Modeling in Financial Risk Management
58 Tech
58 Tech
Sep 24, 2021 · Artificial Intelligence

58.com AI Algorithm Competition: Award Ceremony, Top Teams, and Solution Sharing

The 58.com AI algorithm competition showcased over 210 teams competing to improve job recommendation click‑through and conversion rates, featured an award ceremony with speeches, highlighted the ten winning teams, and presented detailed solution shares—including tree models, feature‑engineering techniques, and deep‑learning approaches—while offering GPU resources on the WPAI platform for continued participation.

AI competitionCTR predictionModel Optimization
0 likes · 10 min read
58.com AI Algorithm Competition: Award Ceremony, Top Teams, and Solution Sharing
Java Interview Crash Guide
Java Interview Crash Guide
Sep 16, 2021 · Artificial Intelligence

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

This article explains the architecture and key components of Toutiao’s recommendation system, covering system overview, content analysis, user tagging, evaluation methods, and content safety measures, and discusses practical implementation details such as feature engineering, model training, recall strategies, and online experimentation.

A/B testingcontent moderationfeature engineering
0 likes · 20 min read
Inside Toutiao’s Recommendation Engine: Architecture, Features, and Safety
DataFunTalk
DataFunTalk
Sep 9, 2021 · Artificial Intelligence

Evolution and Architecture of a Financial Risk Control System: From Monolith to Microservices and Commercialization

This article details the design, refactoring, performance optimization, reliability monitoring, and commercialization of a financial risk control system, covering rule abstraction, decision workflows, feature engineering, model integration, and the trade‑offs between latency and accuracy in large‑scale production environments.

System Architecturedecision enginefeature engineering
0 likes · 15 min read
Evolution and Architecture of a Financial Risk Control System: From Monolith to Microservices and Commercialization
DataFunSummit
DataFunSummit
Aug 29, 2021 · Artificial Intelligence

Zhihu Recommendation Page Ranking: Architecture, Feature Design, Model Evolution, and Practical Insights

This article presents a comprehensive overview of Zhihu's recommendation page ranking system, detailing the request flow, ranking evolution from time‑based to deep‑learning models, feature engineering strategies, model architectures such as DNN, DeepFM, DIN, multi‑task learning, and lessons learned for production deployment.

CTRfeature engineeringmachine learning
0 likes · 12 min read
Zhihu Recommendation Page Ranking: Architecture, Feature Design, Model Evolution, and Practical Insights
Baidu Geek Talk
Baidu Geek Talk
Aug 16, 2021 · Artificial Intelligence

End-to-End Consistency Testing Solution for Click-Through Rate Models in Advertising Systems

The article describes Baidu’s end-to-end consistency testing framework for advertising click-through-rate models, which uses a five-stream verification pipeline and six implementation phases to compare Q-values across feature extraction, table conversions, and DNN computation, enabling precise detection and localization of data and model inconsistencies in production.

BaiduCTR predictionMachine learning testing
0 likes · 17 min read
End-to-End Consistency Testing Solution for Click-Through Rate Models in Advertising Systems
DataFunSummit
DataFunSummit
Jul 26, 2021 · Artificial Intelligence

Deep Learning Ranking System and Model for NetEase News Feed Personalization

This article presents the design, implementation, and optimization of a deep‑learning‑based ranking system for NetEase News, covering pipeline architecture, feature‑processing enhancements, custom TensorFlow operators, and modular model frameworks such as DCN and DIEN to improve recommendation performance.

AIPipelinefeature engineering
0 likes · 11 min read
Deep Learning Ranking System and Model for NetEase News Feed Personalization
DataFunTalk
DataFunTalk
Jul 6, 2021 · Artificial Intelligence

Automated End-to-End Model Iteration in Intelligent Risk Control Systems

This article explains how an intelligent risk control system can achieve fully automated, end-to-end model iteration, detailing the multi-layer architecture, sample and feature selection, automated training, evaluation, scoring, deployment, and the efficiency gains compared with manual processes.

AIModel Evaluationfeature engineering
0 likes · 20 min read
Automated End-to-End Model Iteration in Intelligent Risk Control Systems
Xianyu Technology
Xianyu Technology
Jul 1, 2021 · Artificial Intelligence

Improving Search Relevance in Xianyu: System Design and Model Implementation

The paper describes Xianyu’s new relevance‑matching pipeline—integrating basic, text‑matching, semantic (BERT‑based dual‑tower), multimodal, and click‑graph features and fusing them with a GBDT model—which boosts search DCG@10 by 6.5 %, query satisfaction by 24 % and click interaction by over 20 % while outlining future enhancements for finer attribute matching and richer structured data.

Multimodale‑commercefeature engineering
0 likes · 13 min read
Improving Search Relevance in Xianyu: System Design and Model Implementation
DataFunTalk
DataFunTalk
Apr 22, 2021 · Artificial Intelligence

Governance Algorithms for O2O Platforms: Challenges, Framework, and Model Exploration

This article presents Didi's comprehensive governance algorithm system for O2O platforms, detailing business background, technical challenges, a three‑stage algorithmic framework, model innovations such as small‑sample learning, multi‑task and transfer learning, and extensive feature engineering including multimodal and streaming features.

O2O platformsfeature engineeringgovernance algorithms
0 likes · 15 min read
Governance Algorithms for O2O Platforms: Challenges, Framework, and Model Exploration
Didi Tech
Didi Tech
Apr 16, 2021 · Artificial Intelligence

Governance Algorithms for O2O Ride-Hailing Platforms: Challenges, Framework, and Model Exploration

The paper presents Didi’s three‑layer governance‑algorithm framework for O2O ride‑hailing, addressing high business complexity, limited labeled data, interpretability, and multimodal features through small‑sample, transfer, and multi‑task learning, achieving notable gains in dispute resolution, NPS and CPO while highlighting remaining data and robustness challenges.

Ride Hailingfeature engineeringgovernance algorithms
0 likes · 15 min read
Governance Algorithms for O2O Ride-Hailing Platforms: Challenges, Framework, and Model Exploration
TAL Education Technology
TAL Education Technology
Apr 15, 2021 · Big Data

Global Feature Pool Architecture and Workflow for Data‑Driven Growth

The article describes a unified global feature pool architecture that standardizes offline and real‑time feature production, management, and service layers using Hive, Spark, Flink, Kafka, MySQL, and Hologres to break data silos, improve algorithm development efficiency, and boost growth business performance.

Data Platformdata pipelinefeature engineering
0 likes · 7 min read
Global Feature Pool Architecture and Workflow for Data‑Driven Growth
Ctrip Technology
Ctrip Technology
Apr 9, 2021 · Artificial Intelligence

Algorithm Optimization for Hotel Recommendation and Large‑Scale Discrete DNN Training at Ctrip

This article describes how Ctrip improved hotel recommendation by iterating from logistic regression to GBDT and deep neural networks, designing continuous and discrete features, adopting multi‑task learning with click and conversion signals, and building a large‑scale distributed DNN training and unified feature‑processing framework to boost model accuracy and engineering efficiency.

CtripDNNLarge-Scale Training
0 likes · 15 min read
Algorithm Optimization for Hotel Recommendation and Large‑Scale Discrete DNN Training at Ctrip
Meituan Technology Team
Meituan Technology Team
Mar 4, 2021 · Artificial Intelligence

How Meituan Waimai Scaled Feature Engineering for Billions of Requests

This article details Meituan Waimai's evolution from a simple feature framework to a sophisticated, configurable platform that handles massive feature production, multi‑task scheduling, dynamic protobuf storage, and a model‑feature description language (MFDL) to enable efficient online retrieval, high‑performance computation, and consistent training‑sample generation for its recommendation, advertising, and search services.

MFDLMachine Learning PlatformMeituan
0 likes · 31 min read
How Meituan Waimai Scaled Feature Engineering for Billions of Requests
DataFunTalk
DataFunTalk
Mar 1, 2021 · Artificial Intelligence

Online Learning and Real‑Time Model Updating in JD Retail Search Using Flink

The article describes JD's end‑to‑end online learning pipeline for retail search, covering the background, system architecture, real‑time feature collection, sample stitching, Flink‑based incremental training, parameter updates, and full‑link monitoring to achieve low‑latency, high‑accuracy model serving.

FlinkModel ServingOnline Learning
0 likes · 9 min read
Online Learning and Real‑Time Model Updating in JD Retail Search Using Flink
DataFunTalk
DataFunTalk
Feb 13, 2021 · Artificial Intelligence

Multi-Channel Deep Interest Modeling for 58.com Home Page Recommendations

This article details how 58.com tackled the challenges of multi‑business recommendation on its home page by developing a dual‑channel deep interest model, introducing customized feature‑crossing, optimizing training and online performance, and exploring multi‑channel extensions for broader scenario adaptation.

AIDeep Learningfeature engineering
0 likes · 20 min read
Multi-Channel Deep Interest Modeling for 58.com Home Page Recommendations
DataFunTalk
DataFunTalk
Feb 10, 2021 · Artificial Intelligence

Deep Learning Based Search Ranking Optimization for 58.com Rental Services

This article describes how 58.com’s rental platform leverages deep learning models such as Wide&Deep, DeepFM, DCN, DIN, and DIEN to improve search ranking, detailing data pipelines, feature engineering, model iteration, multi‑task training, prediction optimizations, and resulting online performance gains.

Deep LearningModel Optimizationfeature engineering
0 likes · 27 min read
Deep Learning Based Search Ranking Optimization for 58.com Rental Services
Amap Tech
Amap Tech
Feb 1, 2021 · Artificial Intelligence

AMAP-TECH Algorithm Competition: Dynamic Road Condition Analysis Using In-Vehicle Video

The AMAP‑TECH competition challenged participants to infer real‑time road conditions from in‑vehicle video, prompting the authors to combine lane‑wise vehicle detection with LightGBM and later an end‑to‑end DenseNet‑GRU model, augment data, ensemble five networks, and achieve a 0.7237 F1 score while outlining future deployment and research directions.

Computer VisionDeep LearningModel Deployment
0 likes · 15 min read
AMAP-TECH Algorithm Competition: Dynamic Road Condition Analysis Using In-Vehicle Video
58 Tech
58 Tech
Jan 25, 2021 · Artificial Intelligence

Deep Learning Ranking Models for 58.com Rental Search: Architecture, Model Iterations, and Optimization

This article presents the end‑to‑end design, feature engineering, model evolution (Wide&Deep, DeepFM, DCN, DIN, DIEN), multi‑task training, and deployment optimizations that 58.com applied to improve search ranking for its rental business, demonstrating significant gains in click‑through and conversion rates.

Model Optimizationfeature engineeringmulti-task learning
0 likes · 28 min read
Deep Learning Ranking Models for 58.com Rental Search: Architecture, Model Iterations, and Optimization
DataFunTalk
DataFunTalk
Jan 23, 2021 · Artificial Intelligence

Feature Engineering: Mapping Raw Data to Machine‑Learning Features and Best Practices

This article explains how feature engineering transforms raw data into numerical representations for machine‑learning models, covering mapping of numeric and categorical values, one‑hot and multi‑hot encoding, sparse representations, scaling, handling outliers, binning, data quality checks, and feature interactions to capture non‑linear relationships.

data preprocessingencodingfeature engineering
0 likes · 20 min read
Feature Engineering: Mapping Raw Data to Machine‑Learning Features and Best Practices
Amap Tech
Amap Tech
Jan 15, 2021 · Artificial Intelligence

Solution Overview of the AMAP-TECH Algorithm Competition: Dynamic Road Condition Analysis from In‑Vehicle Video Images

To tackle the AMAP‑TECH competition’s dynamic road‑condition classification from scarce, imbalanced vehicle‑video frames, the team combined YOLOv5 object detection, ResNeXt101‑based semantic embeddings, and engineered temporal detection statistics, feeding the fused features into a five‑fold LightGBM model that achieved top weighted‑F1 performance.

Computer VisionLightGBMMultimodal Learning
0 likes · 10 min read
Solution Overview of the AMAP-TECH Algorithm Competition: Dynamic Road Condition Analysis from In‑Vehicle Video Images
58 Tech
58 Tech
Jan 8, 2021 · Artificial Intelligence

Deep Learning Practices for Multi‑Business Integrated Recommendation: From Dual‑Channel to Multi‑Channel Interest Models and Multi‑Scenario Adaptation

The article details how 58.com tackled the challenges of multi‑business recommendation by evolving its ranking models from a dual‑channel deep interest architecture to a 1+N multi‑channel deep interest model, incorporating customized feature cross layers, scenario‑adaptation mechanisms, and extensive engineering optimizations that yielded significant CTR and conversion gains.

Multi‑Channelfeature engineeringinterest modeling
0 likes · 27 min read
Deep Learning Practices for Multi‑Business Integrated Recommendation: From Dual‑Channel to Multi‑Channel Interest Models and Multi‑Scenario Adaptation
Baobao Algorithm Notes
Baobao Algorithm Notes
Dec 11, 2020 · Industry Insights

How to Cultivate Data Sensitivity: The Core Skill Behind Algorithm Engineers

This article explores the concept of data sensitivity for algorithm engineers, defines its meaning, discusses how to measure it, offers practical steps to develop the skill through data analysis and feature engineering, and reveals the hidden pattern in a label‑prediction example that illustrates its importance.

Industry Insightsalgorithm engineeringdata analysis
0 likes · 6 min read
How to Cultivate Data Sensitivity: The Core Skill Behind Algorithm Engineers
DataFunTalk
DataFunTalk
Nov 19, 2020 · Artificial Intelligence

58 Tongzhen Home Feed Recommendation System: Architecture, Features, and Evolution

This talk details the design, data pipeline, feature engineering, model evolution, and operational insights of the 58 Tongzhen home feed recommendation system, covering its architecture, localization strategies, recall and ranking models, online learning, and future directions for AI-driven content delivery in the down‑market.

AIOnline Learningdown‑market
0 likes · 34 min read
58 Tongzhen Home Feed Recommendation System: Architecture, Features, and Evolution
Xianyu Technology
Xianyu Technology
Nov 17, 2020 · Big Data

Xianyu Premium Product Library: Architecture and Implementation

Xianyu’s premium‑product library combines interpretable, multi‑dimensional metric models built from structured product and user attributes with real‑time and offline pipelines to systematically tag high‑quality items, delivering services via HSF and a message bus, and has driven over 20% click‑through growth and nearly doubled conversion rates.

Real-time Processingdata pipelinefeature engineering
0 likes · 7 min read
Xianyu Premium Product Library: Architecture and Implementation
DataFunSummit
DataFunSummit
Nov 8, 2020 · Artificial Intelligence

Architecture and Evolution of 58 Tongzhen Local Feed Recommendation System

This article details the design, data pipeline, feature engineering, model development, and iterative optimization of the 58 Tongzhen local feed recommendation system, covering business background, user profiling, recall strategies, ranking models such as XGBoost, XDeepFM, and online learning, and future directions.

AIOnline Learningfeature engineering
0 likes · 33 min read
Architecture and Evolution of 58 Tongzhen Local Feed Recommendation System
TAL Education Technology
TAL Education Technology
Sep 17, 2020 · Artificial Intelligence

Comprehensive Guide to Feature Engineering and Data Preprocessing for Machine Learning

This article provides an extensive overview of feature engineering, covering feature understanding, cleaning, construction, selection, transformation, and dimensionality reduction techniques, illustrated with Python code using the Titanic dataset, and offers practical guidelines for improving data quality and model performance in machine learning projects.

PythonTitanic datasetdata preprocessing
0 likes · 44 min read
Comprehensive Guide to Feature Engineering and Data Preprocessing for Machine Learning
DataFunTalk
DataFunTalk
Sep 3, 2020 · Artificial Intelligence

Deep Learning Practices for Click‑Through‑Rate Prediction and Ranking at 58.com

This article describes how 58.com applied deep‑learning techniques—including feature engineering, sample construction, model evolution from Wide&Deep to DIN/DIEN and multi‑task learning—and system‑level optimizations to improve CTR/CPM performance in its large‑scale commercial ranking platform.

CTR predictionDeep LearningSystem optimization
0 likes · 38 min read
Deep Learning Practices for Click‑Through‑Rate Prediction and Ranking at 58.com
58 Tech
58 Tech
Aug 31, 2020 · Artificial Intelligence

Deep Learning Practices for Commercial CTR Prediction at 58.com

This article details the end‑to‑end deep‑learning workflow for click‑through‑rate (CTR) prediction in 58.com’s commercial ranking system, covering system architecture, feature engineering, sample construction, model evolution from Wide&Deep to DIN/DIEN, and engineering optimizations that together yielded significant CPM and CVR improvements.

AdvertisingCTR predictionDeep Learning
0 likes · 38 min read
Deep Learning Practices for Commercial CTR Prediction at 58.com
58 Tech
58 Tech
Aug 24, 2020 · Big Data

Design and Practice of an Online Real-Time Feature System for Intelligent Risk Control

This article presents the concepts, architecture, and practical techniques of an online real‑time feature system used in intelligent risk‑control, covering feature definition, time‑window types, calculation functions, distributed processing, low‑latency storage, and operational challenges in high‑concurrency environments.

Big DataReal-time ProcessingStreaming
0 likes · 16 min read
Design and Practice of an Online Real-Time Feature System for Intelligent Risk Control
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 23, 2020 · Artificial Intelligence

Unlocking Powerful Features: A Deep Dive into Tianchi’s Repeat Purchase Prediction

This tutorial walks through the complete feature‑engineering pipeline for the Alibaba Tianchi “Tmall User Repeat Purchase Prediction” competition, covering data acquisition, memory‑efficient preprocessing, multi‑entity feature construction, statistical aggregations, text vectorisation, embedding generation and stacking‑based model features, all illustrated with Python code and diagrams.

Stackingdata preprocessingfeature engineering
0 likes · 16 min read
Unlocking Powerful Features: A Deep Dive into Tianchi’s Repeat Purchase Prediction
Java Architect Essentials
Java Architect Essentials
Aug 23, 2020 · Industry Insights

Inside 今日头条's Recommendation Engine: Architecture, Features, and Evaluation

This article provides a comprehensive technical overview of 今日头条's recommendation system, covering its three-dimensional feature model, algorithm choices, real‑time training pipeline, recall strategies, content analysis, user tagging, evaluation methods, and content‑safety mechanisms.

A/B testingContent SafetyHierarchical Classification
0 likes · 20 min read
Inside 今日头条's Recommendation Engine: Architecture, Features, and Evaluation
DataFunTalk
DataFunTalk
Aug 20, 2020 · Artificial Intelligence

Weibo Recommendation Algorithm Practice and Machine Learning Platform Evolution

This article shares Weibo’s experience in building and evolving its recommendation algorithms, covering the recommendation scenario, machine learning workflow, feature engineering, model upgrades, large‑scale challenges, deployment via the Weiflow platform, and the capabilities of its machine‑learning infrastructure.

Online LearningWeibofeature engineering
0 likes · 14 min read
Weibo Recommendation Algorithm Practice and Machine Learning Platform Evolution
ITPUB
ITPUB
Jul 23, 2020 · Artificial Intelligence

How Likee Scales Short‑Video Recommendations with Flink, Auto‑Stats, and Cache Tensor

This article details Likee's short‑video recommendation pipeline, covering the evolution of its feature‑engineering framework, the use of Flink for minute‑level statistical and second‑level session features, the integration of automatic statistical features into DNN models, multimodal feature extraction, and the cache‑tensor technique that dramatically improves online inference performance.

AIDeep LearningFlink
0 likes · 18 min read
How Likee Scales Short‑Video Recommendations with Flink, Auto‑Stats, and Cache Tensor
Meituan Technology Team
Meituan Technology Team
Jul 16, 2020 · Artificial Intelligence

Augur: An Online Model Inference Framework and Poker Platform for Meituan Search

Meituan’s AI‑driven search combines the Augur online inference framework—offering stateless, distributed feature operators, transformers, and a DSL for rapid, high‑throughput model scoring—with the Poker platform for model training, versioning, and experimentation, together accelerating iteration, improving performance, and enabling advanced model‑as‑feature ensembles.

AI PlatformModel Servingfeature engineering
0 likes · 26 min read
Augur: An Online Model Inference Framework and Poker Platform for Meituan Search
Alibaba Cloud Developer
Alibaba Cloud Developer
Jul 1, 2020 · Artificial Intelligence

Optimizing Search Timeliness: From Feature Extraction to Ranking Models

This article explains the concept of timeliness in search ranking, defines content and demand side metrics such as half‑life and time sensitivity, describes evaluation criteria, outlines feature extraction and labeling pipelines, and details the multi‑stage modeling, recall, and indexing strategies used to improve timely search results.

Ranking Modelsfeature engineeringinformation retrieval
0 likes · 27 min read
Optimizing Search Timeliness: From Feature Extraction to Ranking Models
DataFunTalk
DataFunTalk
Jun 13, 2020 · Artificial Intelligence

Deep Learning for Expired POI Detection at Amap: Feature Engineering, RNN, Wide&Deep, and Attention‑TCN

This article details how Amap leverages deep‑learning techniques—including temporal and auxiliary feature engineering, multi‑stage RNN models, Wide&Deep architectures, and an Attention‑TCN approach—to accurately identify and handle expired points of interest, improving map freshness and user experience.

Deep LearningPOI expirationRNN
0 likes · 13 min read
Deep Learning for Expired POI Detection at Amap: Feature Engineering, RNN, Wide&Deep, and Attention‑TCN
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.

NLPfeature engineeringmachine learning
0 likes · 9 min read
Semantic Text Understanding for NetEase News Feed Recommendation
JD Tech Talk
JD Tech Talk
Jun 4, 2020 · Artificial Intelligence

The Art and Science of Feature Engineering: Importance, Methods, and Automation

Feature engineering, which occupies the majority of data scientists' time, is essential for building high‑performing machine‑learning models and involves careful data quality control, diverse construction techniques, rigorous selection, and emerging automation efforts, all of which demand domain expertise and systematic practice.

AIdata preprocessingfeature engineering
0 likes · 14 min read
The Art and Science of Feature Engineering: Importance, Methods, and Automation
JD Tech Talk
JD Tech Talk
May 29, 2020 · Artificial Intelligence

The Black Art of Feature Engineering: Importance, Techniques, and Automation

This article explains why feature engineering consumes most of a data scientist's time, outlines its critical steps—including data observation, cleaning, transformation, selection, and reduction—covers practical issues such as missing‑value handling, data leakage, and feature stability, and discusses both manual and automated approaches for building effective machine‑learning models.

data preprocessingfeature engineeringmachine learning
0 likes · 14 min read
The Black Art of Feature Engineering: Importance, Techniques, and Automation
DataFunTalk
DataFunTalk
May 11, 2020 · Artificial Intelligence

Advances in Click‑Through Rate Prediction: Deep Spatio‑Temporal Networks, Memory Networks, and Feature Expression Learning

This article reviews recent innovations in CTR prediction for an intelligent marketing platform, covering deep spatio‑temporal networks, deep memory networks, and a feature‑expression‑assisted learning framework, with system architecture details, experimental results, and references to KDD and IJCAI papers.

AdvertisingCTR predictionDeep Learning
0 likes · 15 min read
Advances in Click‑Through Rate Prediction: Deep Spatio‑Temporal Networks, Memory Networks, and Feature Expression Learning
Amap Tech
Amap Tech
May 8, 2020 · Artificial Intelligence

Expired POI Detection in Amap Using Deep Learning: Feature Engineering, RNN, Wide&Deep, and TCN Models

The project develops a deep‑learning pipeline for Amap’s expired POI detection that integrates two‑year temporal trend features, industry and verification attributes, a variable‑length LSTM, a Wide‑Deep architecture, and a Wide‑Attention Temporal Convolutional Network, achieving higher accuracy and efficiency while outlining future macro‑and micro‑level enhancements.

Deep LearningPOI expirationRNN
0 likes · 15 min read
Expired POI Detection in Amap Using Deep Learning: Feature Engineering, RNN, Wide&Deep, and TCN Models
Python Programming Learning Circle
Python Programming Learning Circle
May 7, 2020 · Artificial Intelligence

Understanding the k-Nearest Neighbor (kNN) Classification Algorithm and Its Python Implementation

This article introduces the concept and intuition behind the k-Nearest Neighbor (kNN) classification algorithm, explains its simple and full forms, discusses feature engineering and Euclidean distance calculations, and provides a complete Python implementation with example code.

classificationeuclidean distancefeature engineering
0 likes · 10 min read
Understanding the k-Nearest Neighbor (kNN) Classification Algorithm and Its Python Implementation
Meituan Technology Team
Meituan Technology Team
Apr 16, 2020 · Artificial Intelligence

Transformer Applications in Meituan Search Ranking: Practice and Experience

Meituan’s search ranking system integrates Transformer‑based models across feature engineering, behavior sequence modeling, and re‑ranking, adapting AutoInt‑style embeddings and multi‑stage attention mechanisms to boost QV_CTR and NDCG, while outlining future enhancements with BERT, graph neural networks, and reinforcement learning.

MeituanTransformerbehavior modeling
0 likes · 16 min read
Transformer Applications in Meituan Search Ranking: Practice and Experience
Youku Technology
Youku Technology
Apr 16, 2020 · Artificial Intelligence

Multimodal Video Classification: Image Feature Improvements and System Insights

The talk presents Alibaba’s hierarchical video‑category system and a multimodal classification pipeline—leveraging EfficientNet, NeXtVLAD fusion, attention‑dropping augmentation, and MoCo contrastive learning—that together boost cold‑start recall by 43%, improve program classification over 20%, and set the stage for larger models and advanced unsupervised methods.

AIEfficientNetMultimodal
0 likes · 17 min read
Multimodal Video Classification: Image Feature Improvements and System Insights
58 Tech
58 Tech
Apr 1, 2020 · Artificial Intelligence

Intelligent Recommendation System for 58 Tongzhen: Architecture, Data, Features, and Model Evolution

This article describes how 58 Tongzhen leverages AI technologies—including data pipelines, feature engineering, various recall and ranking models, and AB‑testing—to build a personalized feed recommendation system for the down‑market, detailing its overall architecture, data sources, model iterations, performance gains, and future directions.

AB testingAIDeep Learning
0 likes · 20 min read
Intelligent Recommendation System for 58 Tongzhen: Architecture, Data, Features, and Model Evolution
HomeTech
HomeTech
Mar 18, 2020 · Artificial Intelligence

Automobile Home Recommendation System Architecture and Ranking Models

This article presents a comprehensive overview of the Automobile Home recommendation system, detailing its objectives, architecture, various ranking models from LR to DeepFM, online learning mechanisms, service APIs, feature engineering pipelines, model training platforms, debugging tools, and future optimization directions.

AB testingAutoMLOnline Learning
0 likes · 18 min read
Automobile Home Recommendation System Architecture and Ranking Models
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
Xueersi Online School Tech Team
Xueersi Online School Tech Team
Mar 13, 2020 · Artificial Intelligence

Predictive Modeling of Student Renewal and Refund Intentions Using Logistic Regression in Online Education

This article describes how logistic regression models are built, iterated, and applied to predict student renewal and refund behavior in an online school, detailing data collection, feature engineering, model training, evaluation, and how the predictions are used to recommend interventions for teachers.

Education Analyticsfeature engineeringlogistic regression
0 likes · 9 min read
Predictive Modeling of Student Renewal and Refund Intentions Using Logistic Regression in Online Education
Qunar Tech Salon
Qunar Tech Salon
Mar 13, 2020 · Artificial Intelligence

The Evolution of AutoHome Recommendation System Ranking Algorithms

This article details the architecture, model evolution, feature processing, online learning, and future optimization plans of AutoHome's recommendation system, covering stages from resource collection to ranking, various models such as LR, XGBoost, FM, DeepFM, and operational practices like AB testing and debugging.

Online Learningfeature engineeringranking algorithm
0 likes · 18 min read
The Evolution of AutoHome Recommendation System Ranking Algorithms
DataFunTalk
DataFunTalk
Mar 12, 2020 · Artificial Intelligence

Model Evolution and Optimization for Recommendation Systems in a Mid‑size E‑commerce App

This article describes the end‑to‑end recommendation pipeline of the Province Money Fast Report app, covering business background, data collection, model training and evaluation, the evolution from FM to DeepFM, DIN, DCN, xDeepFM, ESMM and custom networks, as well as serving strategies and practical lessons learned.

CTR predictionDeep LearningModel Serving
0 likes · 28 min read
Model Evolution and Optimization for Recommendation Systems in a Mid‑size E‑commerce App
ITPUB
ITPUB
Mar 11, 2020 · Artificial Intelligence

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

This article provides a comprehensive technical overview of Toutiao’s recommendation system, covering its three‑dimensional modeling approach, feature engineering, user‑tag pipelines, real‑time training infrastructure, evaluation methodology, and content‑safety mechanisms.

A/B testingContent SafetyReal-time Training
0 likes · 17 min read
Inside Toutiao’s Recommendation Engine: Architecture, Features, and Evaluation
Qudian (formerly Qufenqi) Technology Team
Qudian (formerly Qufenqi) Technology Team
Mar 4, 2020 · Artificial Intelligence

How Intelligent Marketing Leverages AI and Big Data to Boost Conversion Rates

This article explains how intelligent marketing transforms traditional, labor‑intensive strategies into data‑driven, AI‑powered systems by detailing the multi‑layer architecture, data pipelines, machine‑learning models such as LR and GBDT+LR, and future directions like personalized copy generation and deep‑learning enhancements.

AIMarketingdata engineering
0 likes · 8 min read
How Intelligent Marketing Leverages AI and Big Data to Boost Conversion Rates
Architecture Digest
Architecture Digest
Mar 2, 2020 · Artificial Intelligence

Recommendation System Architecture and Practices at Toutiao

This article provides a comprehensive overview of Toutiao's recommendation system, covering its three-dimensional modeling of content, user, and environment features, various algorithmic approaches, feature extraction, real‑time training pipelines, recall strategies, user‑tag engineering, evaluation methods, and content‑safety measures.

A/B testingContent SafetyReal-time Training
0 likes · 18 min read
Recommendation System Architecture and Practices at Toutiao
DataFunTalk
DataFunTalk
Feb 28, 2020 · Artificial Intelligence

Evolution of Autohome's Recommendation System Ranking Algorithms

The article details the five‑year evolution of Autohome's recommendation system, covering its overall architecture, the progression of ranking models from LR to DeepFM and online learning, feature engineering pipelines, ranking service APIs, AB testing practices, and future optimization directions.

AB testingAIOnline Learning
0 likes · 20 min read
Evolution of Autohome's Recommendation System Ranking Algorithms
37 Interactive Technology Team
37 Interactive Technology Team
Feb 20, 2020 · Artificial Intelligence

Risk Control System for Detecting Game Account Fraud Using Feature Engineering and Graph Database

The article describes a risk‑control pipeline for detecting high‑volume fraudulent game accounts, detailing data collection from game logs, extensive feature engineering and statistical tests, enrichment via a Neo4j knowledge graph, and a hybrid RandomForest‑GBDT model combined with methods to filter personal accounts.

Graph DatabaseNeo4jdata mining
0 likes · 8 min read
Risk Control System for Detecting Game Account Fraud Using Feature Engineering and Graph Database
21CTO
21CTO
Feb 18, 2020 · Artificial Intelligence

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

This article details Toutiao’s large‑scale recommendation system, explaining how it models content, user, and environment features, the variety of algorithms and real‑time training pipelines used, feature engineering categories, recall strategies, content analysis, user tagging, evaluation methods, and content‑safety mechanisms.

Content SafetyEvaluationReal-time Training
0 likes · 18 min read
Inside Toutiao’s Real‑Time Recommendation Engine: Architecture, Features, and Evaluation
Meituan Technology Team
Meituan Technology Team
Feb 6, 2020 · Artificial Intelligence

Building a One-Stop Machine Learning Platform: Meituan's Turing Platform

Meituan’s Turing platform consolidates the entire delivery‑order workflow—from massive data ingestion and feature generation to model training, evaluation, deployment, real‑time prediction, and AB testing—into a single, end‑to‑end system that evolved from a minimal MVP into a fully platformized solution, addressing speed, accuracy, and engineering‑algorithm decoupling while planning deeper deep‑learning integration.

AB testingDeep LearningMachine Learning Platform
0 likes · 16 min read
Building a One-Stop Machine Learning Platform: Meituan's Turing Platform
Huajiao Technology
Huajiao Technology
Jan 21, 2020 · Artificial Intelligence

Overview of Ranking Algorithms in Recommendation Systems

This article reviews the evolution of ranking models in modern recommendation systems, covering traditional linear models, factorization machines, tree‑based GBDT+LR, and a range of deep learning architectures such as Wide&Deep, DeepFM, DCN, xDeepFM, DIN, as well as multi‑task frameworks like ESMM and MMOE, and finally illustrates their practical deployment in a live streaming platform.

Deep Learningfeature engineeringmachine learning
0 likes · 20 min read
Overview of Ranking Algorithms in Recommendation Systems
Mafengwo Technology
Mafengwo Technology
Jan 16, 2020 · Artificial Intelligence

How Machine Learning Transforms Hotel Aggregation for Real‑Time Accurate Pricing

This article explains the evolution of hotel aggregation at Mafengwo, from simple cosine similarity matching to advanced machine‑learning pipelines using tokenization, feature engineering, and LightGBM models, highlighting challenges of accuracy and real‑time performance and presenting practical solutions.

Cosine SimilarityLightGBMfeature engineering
0 likes · 16 min read
How Machine Learning Transforms Hotel Aggregation for Real‑Time Accurate Pricing
360 Quality & Efficiency
360 Quality & Efficiency
Dec 20, 2019 · Artificial Intelligence

Automated APK Test Script Recommendation: Data Processing and Model Training Pipeline

This article describes a complete pipeline for recommending automated test scripts for APK releases, covering CSV data preprocessing, feature encoding, tokenization with pkuseg and jieba, and training various machine‑learning models such as LDA, word2vec, XGBoost, deep neural networks, and multi‑label classifiers to predict script execution order.

APK testingDeep LearningModel Training
0 likes · 14 min read
Automated APK Test Script Recommendation: Data Processing and Model Training Pipeline
NetEase Game Operations Platform
NetEase Game Operations Platform
Dec 7, 2019 · Operations

Intelligent Anomaly Detection for Operations Maintenance: Machine Learning Methods and Workflow

This article explains the importance of operations maintenance, outlines the challenges of traditional rule‑based anomaly detection, and describes how machine‑learning‑driven AIOps—including feature engineering, unsupervised and supervised models—can provide more accurate, scalable, and automated detection of server anomalies.

Operationsaiopsfeature engineering
0 likes · 10 min read
Intelligent Anomaly Detection for Operations Maintenance: Machine Learning Methods and Workflow
Tencent Cloud Developer
Tencent Cloud Developer
Dec 3, 2019 · Artificial Intelligence

Feature Engineering Practices for Short‑Video Recommendation Systems

Effective short‑video recommendation relies on meticulous feature engineering that transforms raw signals—numerical counts, categorical IDs, content and user embeddings, context and session data—through bucketization, scaling, crossing, and smoothing, then selects and evaluates them via filtering, wrapping, regularization, and importance analysis to mitigate business biases and improve multi‑objective ranking performance.

Embeddingbias mitigationdata preprocessing
0 likes · 32 min read
Feature Engineering Practices for Short‑Video Recommendation Systems
58 Tech
58 Tech
Nov 29, 2019 · Artificial Intelligence

Ranking Strategy Optimization Practices for Commercial Traffic at 58.com

This article details the end‑to‑end optimization of 58.com’s commercial traffic ranking system, covering data‑flow upgrades, advanced feature engineering, real‑time and multi‑task model improvements, and a multi‑factor ranking mechanism, while sharing practical results and future directions.

Real-time Data Pipelinefeature engineeringmachine learning
0 likes · 17 min read
Ranking Strategy Optimization Practices for Commercial Traffic at 58.com
58 Tech
58 Tech
Nov 15, 2019 · Artificial Intelligence

From Zero to One: Building a Personalized Recommendation System for 58.com Recruitment Platform

This article presents a comprehensive case study of how 58.com built a personalized recommendation system for its large‑scale recruitment platform, covering business background, data challenges, user modeling, recall strategies, ranking pipelines, system architecture, experimental infrastructure, and future research directions.

AB testingfeature engineeringknowledge graph
0 likes · 18 min read
From Zero to One: Building a Personalized Recommendation System for 58.com Recruitment Platform
58 Tech
58 Tech
Nov 11, 2019 · Artificial Intelligence

Design and Implementation of the 58 Car Price Estimation System Using Machine Learning

The article describes the end‑to‑end architecture, data collection, preprocessing, feature engineering, model selection, training, and hyper‑parameter tuning of 58’s car price estimation platform, which leverages Spark, XGBoost, LightGBM and custom business rules to predict vehicle resale values.

LightGBMXGBoostcar price estimation
0 likes · 11 min read
Design and Implementation of the 58 Car Price Estimation System Using Machine Learning
Mafengwo Technology
Mafengwo Technology
Nov 7, 2019 · Artificial Intelligence

Inside MaFengWo’s Scalable Ranking Platform: Architecture, Verification & Explainability

This article explains how MaFengWo’s recommendation system combines recall, ranking, and rerank stages, details the evolution of its sorting algorithm platform, and shows how data verification and model‑explainability techniques like SHAP and LIME improve online performance and accelerate model iteration.

Data verificationModel ExplainabilityXGBoost
0 likes · 13 min read
Inside MaFengWo’s Scalable Ranking Platform: Architecture, Verification & Explainability
DataFunTalk
DataFunTalk
Nov 4, 2019 · Artificial Intelligence

Standardizing Model Training and Feature Processing in Recommendation Systems

This article describes a standardized workflow for feature collection, configuration, processing, and model training/prediction in large‑scale recommendation systems, using CSV‑based definitions and code generation to ensure consistency between offline training and online serving while reducing manual coding effort.

CTR predictionModel Trainingfeature engineering
0 likes · 14 min read
Standardizing Model Training and Feature Processing in Recommendation Systems
58 Tech
58 Tech
Oct 28, 2019 · Artificial Intelligence

Ranking Strategy Optimization Practice in 58 Commercial Traffic

This article details the comprehensive optimization of 58's commercial traffic ranking system, covering data‑flow upgrades, advanced feature engineering, model enhancements—including online training, multi‑task and relevance models—and a multi‑factor ranking mechanism that together improve monetization efficiency and user experience.

Real-time Data Pipelinee‑commercefeature engineering
0 likes · 16 min read
Ranking Strategy Optimization Practice in 58 Commercial Traffic
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