Tagged articles

1881 articles

Page 16 of 19

Dec 12, 2018 · Artificial Intelligence

Tackling Pseudo-Exposure in Mobile E-Commerce: A Contextual Multiple-Play Bandit Approach

To address the pseudo-exposure problem that reduces click-through rates in mobile e-commerce recommendation, the authors model the task as a contextual multiple-play bandit, propose weighted sample and similarity-enhanced linear reward extensions, provide sublinear regret proofs, and demonstrate significant CTR gains on real Taobao data.

Bandit AlgorithmsCTR optimizationcontextual multi-play

0 likes · 30 min read

Tackling Pseudo-Exposure in Mobile E-Commerce: A Contextual Multiple-Play Bandit Approach

Efficient Ops

Dec 11, 2018 · Operations

How Alibaba’s AI‑Powered Monitoring Tackles Complex Business Anomalies

In this talk, Alibaba senior tech expert Wang Zhaogang explains how intelligent monitoring, powered by machine‑learning algorithms and multi‑metric analysis, addresses the challenges of diverse business scenarios, enhances anomaly detection, improves root‑cause analysis, and shapes the future of smart operations.

OperationsRoot Cause Analysisanomaly detection

0 likes · 23 min read

How Alibaba’s AI‑Powered Monitoring Tackles Complex Business Anomalies

JD Tech

Dec 6, 2018 · Operations

Shortening Decision Chains: End-to-End Inventory Management and Intelligent Replenishment in JD's Supply Chain

JD's chief scientist Shen Zuo‑jun explains how shortening the decision chain with end‑to‑end algorithms and intelligent multi‑level replenishment dramatically improves inventory turnover, stock availability, and forecasting accuracy, showcasing a novel supply‑chain research direction that integrates AI, big data, and human expertise.

End-to-EndOperationsforecasting

0 likes · 9 min read

Shortening Decision Chains: End-to-End Inventory Management and Intelligent Replenishment in JD's Supply Chain

Beike Product & Technology

Dec 6, 2018 · Artificial Intelligence

Real Estate Rental Platform: True Listing Model and Credit System Construction

This presentation details how Beike Rental leverages big data and machine‑learning techniques to detect non‑authentic listings, build a four‑criterion true‑listing model, develop pricing and image‑analysis models, and design a merchant credit scoring system that improves service quality and market efficiency.

Credit ScoringData QualityImage Analysis

0 likes · 27 min read

Real Estate Rental Platform: True Listing Model and Credit System Construction

Tencent Cloud Developer

Dec 5, 2018 · Artificial Intelligence

19 AI Technologies That Are Currently Dominating

The article surveys the nineteen leading AI technologies—from natural language generation and speech recognition to digital twins and marketing automation—detailing their core functions, common use cases such as customer service, security, content creation, and the key vendors delivering each solution.

AI TechnologiesArtificial IntelligenceComputer Vision

0 likes · 17 min read

19 AI Technologies That Are Currently Dominating

21CTO

Dec 3, 2018 · Information Security

Can Your Keyboard Secrets Be Heard? Inside the Keytap Acoustic Attack

This article explains how the open‑source Keytap project captures short audio snippets from a microphone to reconstruct typed characters, outlines its four‑step process of data collection, model building, keystroke detection, and character identification, and compares it with related acoustic eavesdropping research.

Information Securityacoustic side-channelaudio keylogging

0 likes · 8 min read

Can Your Keyboard Secrets Be Heard? Inside the Keytap Acoustic Attack

MaGe Linux Operations

Nov 30, 2018 · Artificial Intelligence

Avoid These Common NumPy Pitfalls When Doing Machine Learning

This article examines frequent traps when using NumPy for matrix operations in machine learning, comparing its quirks to MATLAB/Octave and offering practical insights to prevent shape errors, inefficient indexing, confusing syntax, and unintuitive code patterns.

NumPyPythondata analysis

0 likes · 7 min read

Avoid These Common NumPy Pitfalls When Doing Machine Learning

Liulishuo Tech Team

Nov 29, 2018 · Cloud Native

Building an Efficient Machine Learning Training Platform on Kubernetes

This article describes how the Liulishuo algorithm team designed and implemented a Kubernetes‑based training platform that addresses the iterative, data‑intensive, and resource‑dynamic characteristics of machine learning workloads by pooling resources, enabling rapid provisioning, and optimizing scheduling and storage.

Cloud NativeKubernetesmachine learning

0 likes · 9 min read

Building an Efficient Machine Learning Training Platform on Kubernetes

37 Interactive Technology Team

Nov 27, 2018 · Artificial Intelligence

37 Xiao Luban: A Machine‑Learning Linear Regression System for Automatic Banner Generation

The article describes a PHP engineer who built a machine‑learning linear regression system called 37 Xiao Luban to automatically generate game banner images, cutting production time from hours to minutes, using polynomial regression on collected scaling data, achieving 80‑90% usability.

AIBanner GenerationImage Processing

0 likes · 7 min read

37 Xiao Luban: A Machine‑Learning Linear Regression System for Automatic Banner Generation

MaGe Linux Operations

Nov 26, 2018 · Artificial Intelligence

Master Python Machine Learning in 14 Steps: From Zero to Expert

This comprehensive guide walks beginners through fourteen practical steps to learn Python machine learning, covering essential Python skills, core scientific libraries, fundamental algorithms, advanced techniques like SVM and ensemble methods, dimensionality reduction, and deep learning with TensorFlow, all using free online resources.

Data ScienceDeep LearningPython

0 likes · 22 min read

Master Python Machine Learning in 14 Steps: From Zero to Expert

Alibaba Cloud Developer

Nov 21, 2018 · Artificial Intelligence

How Unsupervised Autoencoders Boost International Credit Card Fraud Detection

International credit card fraud, a growing threat, can be more effectively identified by applying unsupervised autoencoder models, which outperform traditional rule‑based systems by tripling recall and increasing accuracy by 40%, while reducing maintenance costs and adapting to new fraud patterns.

AutoencoderUnsupervised Learninganomaly detection

0 likes · 9 min read

How Unsupervised Autoencoders Boost International Credit Card Fraud Detection

DataFunTalk

Nov 21, 2018 · Artificial Intelligence

Personalized Recommendation System of 51 Credit Card: Architecture, Challenges, and Growth Cases

This article details how 51 Credit Card leverages artificial intelligence to build a personalized recommendation system, covering business pain points, technical challenges, a three‑layer tagging architecture from bill and app data, model deployment pipelines, and real‑world growth case studies that boosted conversion and ROI.

AIdata engineeringfinance

0 likes · 14 min read

Personalized Recommendation System of 51 Credit Card: Architecture, Challenges, and Growth Cases

Programmer DD

Nov 21, 2018 · Artificial Intelligence

What I Learned From My AI Engineer Interview: Recommendation Systems, TF‑IDF, Word2Vec & SVM Explained

A Java developer shares his self‑learning journey into AI, recounts a technical interview covering recommendation system types, TF‑IDF similarity metrics, word2vec behavior modeling, and SVM fundamentals, and reflects on the challenges and resources that helped him transition into algorithm engineering.

AIinterviewmachine learning

0 likes · 7 min read

What I Learned From My AI Engineer Interview: Recommendation Systems, TF‑IDF, Word2Vec & SVM Explained

Tencent Cloud Developer

Nov 20, 2018 · Artificial Intelligence

Top 18 Machine Learning Platforms Every Developer Should Know

This guide lists and briefly describes 18 open‑source and cloud‑based machine learning platforms—from H2O and TensorFlow to Azure ML and AWS services—highlighting their key features, supported languages, and typical use cases for developers at any skill level.

AI toolsDeep LearningDevelopment

0 likes · 12 min read

Top 18 Machine Learning Platforms Every Developer Should Know

Tencent Cloud Developer

Nov 19, 2018 · Artificial Intelligence

10 Open-Source Tools and Frameworks for Artificial Intelligence

The article surveys ten leading open-source AI tools and frameworks—including TensorFlow, SystemML, Caffe, Apache Mahout, OpenNN, Torch, Neuroph, Deeplearning4j, Mycroft, and OpenCog—detailing their primary features, supported languages, hardware compatibility, and typical use cases for research and development.

Artificial IntelligenceDeep LearningOpen-source

0 likes · 12 min read

10 Open-Source Tools and Frameworks for Artificial Intelligence

Alibaba Cloud Developer

Nov 16, 2018 · Artificial Intelligence

How Alibaba’s Search Engine Evolved Over a Decade of Double‑11: From Offline Models to Real‑Time AI

This article traces the ten‑year evolution of Alibaba’s e‑commerce search system, detailing four major stages—from the early Pora streaming engine to dual‑link real‑time architectures, the integration of deep and reinforcement learning, and the shift to large‑scale online deep learning—while highlighting the technical drivers and future AI‑enabled search vision.

Online Learninge‑commercemachine learning

0 likes · 16 min read

How Alibaba’s Search Engine Evolved Over a Decade of Double‑11: From Offline Models to Real‑Time AI

Youku Technology

Nov 13, 2018 · Industry Insights

How Youku Delivered Seamless 4K Live Streaming for 25 Million Viewers on Cat Night

Youku’s Cat Night live broadcast on November 10 attracted 25 million viewers and leveraged intelligent bandwidth control, machine‑learning‑based video encoding, and a 50 fps high‑frame‑rate engine to deliver seamless 4K streaming and interactive gaming experiences without lag.

Video EncodingYoukubandwidth management

0 likes · 5 min read

How Youku Delivered Seamless 4K Live Streaming for 25 Million Viewers on Cat Night

dbaplus Community

Nov 11, 2018 · Operations

How 360 Built an AI‑Powered Ops System to Cut Costs and Boost Efficiency

360’s AI‑ops team shares a year‑long journey of turning massive operational data into intelligent solutions—covering background, their AIOps philosophy, practical modules like capacity forecasting, host classification, resource reclamation, smart MySQL scheduling, anomaly detection, alarm reduction, and root‑cause analysis—to dramatically improve cost, efficiency, and reliability.

Capacity Forecastingaiopsanomaly detection

0 likes · 16 min read

How 360 Built an AI‑Powered Ops System to Cut Costs and Boost Efficiency

Efficient Ops

Nov 10, 2018 · Fundamentals

Essential Tech Books Every Engineer Should Read – From Linux to AI

This article presents a curated selection of must‑read technical books for engineers, spanning programming fundamentals, Linux kernel internals, Go development, web performance, cloud native Kubernetes, DevOps, databases, and machine learning, each accompanied by concise expert insights.

BooksDevOpscloud

0 likes · 5 min read

Essential Tech Books Every Engineer Should Read – From Linux to AI

Alibaba Cloud Infrastructure

Nov 9, 2018 · Artificial Intelligence

Predicting Server Memory Failures with Machine Learning: Feature Selection, Data Preprocessing, and Model Evaluation

This article presents a machine‑learning approach to predict DRAM failures in large‑scale data centers by analyzing server logs, selecting state, log, and static features through statistical tests and mutual information, preprocessing the data, and employing a tree‑based ensemble classifier that outperforms industry baselines.

Predictive Maintenanceclassificationfeature selection

0 likes · 7 min read

Predicting Server Memory Failures with Machine Learning: Feature Selection, Data Preprocessing, and Model Evaluation

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.

Model Optimizationclick-through ratefeature engineering

0 likes · 23 min read

Search List Ranking Efficiency Optimization Practices at 58.com

Alibaba Cloud Developer

Nov 9, 2018 · Databases

Inside the ACM Distinguished Scientist Award: Alibaba’s Li Fei‑Fei on Database Innovation

In November 2018, ACM honored Alibaba Damo Academy’s chief database scientist Li Fei‑Fei with the Distinguished Scientist award, recognizing his pioneering work on distributed, intelligent, and secure database systems, his transition from academia to industry, and his vision for future cloud‑native database technologies and academia‑industry collaboration.

Cloud ComputingIndustry-Academia Collaborationdatabases

0 likes · 11 min read

Inside the ACM Distinguished Scientist Award: Alibaba’s Li Fei‑Fei on Database Innovation

Alibaba Cloud Infrastructure

Nov 7, 2018 · Artificial Intelligence

Hard Disk Failure Prediction Architecture and Methods Based on SMART Attributes and Machine Learning

The article presents a comprehensive hard‑disk failure prediction framework that addresses data scale, environment, and quality challenges by combining domain‑threshold statistics, wear‑out kink analysis, and parallel machine‑learning models using SMART parameters to improve recall while reducing false alarms.

SMARTfailure predictionhard disk

0 likes · 10 min read

Hard Disk Failure Prediction Architecture and Methods Based on SMART Attributes and Machine Learning

DataFunTalk

Nov 7, 2018 · Artificial Intelligence

Evolution of Ele.me Recommendation Algorithms and Online Learning Practice

This article outlines the background of Ele.me's recommendation business, details the evolution of its recommendation algorithms from rule‑based models to deep learning and online learning, and explains the practical implementation of real‑time data pipelines, feature engineering, model training, and deployment.

Ele.meOnline Learningmachine learning

0 likes · 13 min read

Evolution of Ele.me Recommendation Algorithms and Online Learning Practice

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.

Web Crawlingbinary classificationfeature engineering

0 likes · 13 min read

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

Xianyu Technology

Nov 2, 2018 · Artificial Intelligence

FireEye AI-Powered Automated Testing Framework: Architecture, Model Selection, and Retraining

FireEye is an AI‑driven automated UI testing framework that ingests simulated and real screenshots, preprocesses images and OCR text, and employs a CNN for page anomalies, an SSD detector for control anomalies, and an LSTM‑based classifier for text anomalies, with Jenkins‑triggered retraining, cloud model storage, and API serving, aiming to simplify testing and enable future AutoML enhancements.

AIAutomated TestingKeras

0 likes · 9 min read

FireEye AI-Powered Automated Testing Framework: Architecture, Model Selection, and Retraining

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 PlatformWPAIfeature engineering

0 likes · 15 min read

Overview of the WPAI AI Platform Architecture and Implementation

Youku Technology

Oct 29, 2018 · Artificial Intelligence

Improving Online Video Experience: Youku’s End‑to‑End Video Quality Enhancement Techniques

Youku enhances online video by applying intelligent post‑production contrast mapping, device‑specific HDR tone‑mapping, high‑frame‑rate restoration through frame‑rate conversion, and ROI‑aware encoding that allocates bitrate to key visual areas, complemented by audio processing, to deliver cinema‑grade quality across diverse screens.

HDRROI encodingStreaming

0 likes · 9 min read

Improving Online Video Experience: Youku’s End‑to‑End Video Quality Enhancement Techniques

360 Quality & Efficiency

Oct 26, 2018 · Artificial Intelligence

Machine Learning Methods: Discriminative and Generative Models, Semi‑Supervised Learning, and GAN‑Based Classification

This article explains the distinction between discriminative and generative models, outlines the challenges of limited labeled data, introduces semi‑supervised learning principles, and describes GAN‑based semi‑supervised classification algorithms with illustrative diagrams.

Artificial IntelligenceGANGenerative Models

0 likes · 3 min read

Machine Learning Methods: Discriminative and Generative Models, Semi‑Supervised Learning, and GAN‑Based Classification

21CTO

Oct 25, 2018 · Artificial Intelligence

How Recommender Systems Work: From Basics to a Python Demo

This article explains what recommender systems are, their evolution, when to use them, the main techniques—including collaborative filtering, content‑based and knowledge‑based approaches—addresses cold‑start challenges, and provides a step‑by‑step Python implementation with code examples.

AIPythoncollaborative filtering

0 likes · 15 min read

How Recommender Systems Work: From Basics to a Python Demo

Tencent Cloud Developer

Oct 23, 2018 · Artificial Intelligence

Demystifying AI, Machine Learning, and Deep Learning

The article clarifies that artificial intelligence encompasses machine learning, which in turn includes deep learning, and uses real‑world examples—from fraud detection and customer clustering to image recognition and language translation—to illustrate how these data‑driven models learn patterns, make predictions, and transform many industries.

Artificial IntelligenceData ScienceDeep Learning

0 likes · 12 min read

Demystifying AI, Machine Learning, and Deep Learning

MaGe Linux Operations

Oct 19, 2018 · Artificial Intelligence

Why Numpy’s Array vs Matrix Can Trip Up Your Machine Learning Projects

The article examines common pitfalls when using NumPy arrays and matrices for data manipulation in machine learning, highlighting chaotic data structures, inefficient filtering, confusing arithmetic syntax, and unintuitive code patterns compared to MATLAB/Octave, and concludes with a critique of Python’s ergonomics.

NumPyPythondata-processing

0 likes · 7 min read

Why Numpy’s Array vs Matrix Can Trip Up Your Machine Learning Projects

DataFunTalk

Oct 17, 2018 · Artificial Intelligence

Design Principles for AI‑Driven Anti‑Fraud Systems

The article outlines Tongdun Technology's anti‑fraud challenges, presents their AI‑based detection solutions, and details design principles—including early warning, multi‑feature analysis, and human‑machine collaboration—to build a robust, multi‑layered fraud prevention framework.

AI designRisk DetectionUnsupervised Learning

0 likes · 10 min read

Design Principles for AI‑Driven Anti‑Fraud Systems

Qunar Tech Salon

Oct 15, 2018 · Artificial Intelligence

Introduction to Decision Trees with scikit-learn

This article provides a comprehensive guide to decision tree algorithms, covering their theoretical background, classic use‑cases, scikit‑learn's DecisionTreeClassifier parameters, step‑by‑step Python examples for training, visualizing, and exporting trees, as well as a comparison of ID3, C4.5, and CART methods with their advantages and limitations.

Pythonclassificationdecision tree

0 likes · 20 min read

Introduction to Decision Trees with scikit-learn

Hulu Beijing

Oct 12, 2018 · Artificial Intelligence

How Hulu Boosted Recommendation Diversity with Determinantal Point Processes

This article explains how Hulu tackled the trade‑off between accuracy and diversity in its massive video recommendation system by applying Determinantal Point Processes and an efficient incremental greedy algorithm, achieving 100× speed‑ups without sacrificing recommendation quality.

DiversityHuludeterminantal point process

0 likes · 7 min read

How Hulu Boosted Recommendation Diversity with Determinantal Point Processes

Tencent Cloud Developer

Oct 11, 2018 · Artificial Intelligence

Demystifying Neural Networks: A Mathematical Approach (Part 1)

The article mathematically demystifies neural networks by first illustrating a linear predictor for kilometre‑to‑mile conversion and a basic bug classifier, then exposing the limits of single linear boundaries (e.g., XOR), before introducing artificial neurons, activation functions, and multi‑layer weight‑adjustment training.

Artificial NeuronPredictionactivation functions

0 likes · 15 min read

Demystifying Neural Networks: A Mathematical Approach (Part 1)

Alibaba Cloud Developer

Oct 10, 2018 · Artificial Intelligence

How Alibaba’s Uni‑Marketing Boosted Brand Conversions with AI‑Driven Audience Selection

This article details Alibaba's Uni‑Marketing case study where a brand‑targeted audience selection algorithm, built on big‑data and AI techniques, improved the O→IPL deepening rate by 47% during the New‑Year Festival, outlining the technical pipeline, models, evaluation metrics, challenges, and future directions.

Big DataDigital Marketingbrand optimization

0 likes · 20 min read

Qunar Tech Salon

Oct 10, 2018 · Artificial Intelligence

Introduction to Lasso Regression with scikit-learn

This article provides a comprehensive guide to Lasso regression, covering its theoretical background, scikit-learn API parameters, step‑by‑step Python implementation, cross‑validation for hyper‑parameter tuning, visualization of predictions, and a discussion of its advantages over ridge regression.

Cross‑ValidationData visualizationPython

0 likes · 6 min read

Introduction to Lasso Regression with scikit-learn

Qunar Tech Salon

Oct 9, 2018 · Artificial Intelligence

Ridge Regression with scikit-learn: Theory, Implementation, and Example

This article introduces Ridge regression, explains its theory and regularization role, discusses overfitting and bias‑variance trade‑offs, presents scikit‑learn parameters, and provides a complete Python example from data loading to model training, evaluation, and optimal alpha selection.

PythonRegularizationmachine learning

0 likes · 7 min read

Ridge Regression with scikit-learn: Theory, Implementation, and Example

AI Large-Model Wave and Transformation Guide

Oct 8, 2018 · Artificial Intelligence

Build a CART Decision Tree from Scratch in Python – Full Step‑by‑Step Guide

This article walks through a complete Python implementation of the CART decision‑tree algorithm on the Banknote dataset, covering data loading, cross‑validation splitting, Gini impurity calculation, recursive tree construction, prediction, and performance evaluation with concrete code examples.

Banknote DatasetCARTGini Index

0 likes · 7 min read

Build a CART Decision Tree from Scratch in Python – Full Step‑by‑Step Guide

Alibaba Cloud Developer

Sep 30, 2018 · Artificial Intelligence

How Alibaba’s Search & Recommendation Evolved: From Rules to Cognitive AI

This article reviews the evolution of Alibaba’s e‑commerce search and recommendation technologies, detailing Taobao’s unique challenges, the shift from rule‑based retrieval to large‑scale machine learning, real‑time online learning, deep learning and intelligent decision‑making, and outlines future directions toward cognitive intelligence.

Deep Learningcognitive AIe‑commerce

0 likes · 16 min read

How Alibaba’s Search & Recommendation Evolved: From Rules to Cognitive AI

JD Tech

Sep 29, 2018 · Artificial Intelligence

JD.com Prediction Technology: Architecture, Applications, and Future Directions

The article outlines JD.com's evolution of prediction technology from early book‑category sales forecasting to a comprehensive AI‑driven platform that supports sales, order, and GMV forecasts, describes its modular architecture and core algorithm choices, and discusses future enhancements for smarter supply‑chain collaboration.

Big DataPredictionforecasting

0 likes · 6 min read

JD.com Prediction Technology: Architecture, Applications, and Future Directions

Xianyu Technology

Sep 25, 2018 · Artificial Intelligence

TensorFlow Lite Applications and UI2Code at Xianyu (Idle Fish)

Xianyu leverages a custom TensorFlow Lite framework to power AI‑driven features such as dynamic video‑cover selection, video fingerprinting, and furniture recognition for smart rentals, while its UI2Code tool transforms screenshots into pixel‑perfect production UI code, emphasizing extensibility, security, and online model updates.

TensorFlowTensorFlow LiteXianyu

0 likes · 7 min read

TensorFlow Lite Applications and UI2Code at Xianyu (Idle Fish)

21CTO

Sep 24, 2018 · Artificial Intelligence

Why Recommendation Algorithms Aren’t Magic: A Practical Guide

This article explains the fundamentals of recommendation algorithms, illustrates their modest impact with real‑world examples, and outlines how modern e‑commerce systems collect data, rank items, and use rapid A/B testing to continuously improve personalized recommendations.

A/B testingalgorithm designe‑commerce

0 likes · 10 min read

Why Recommendation Algorithms Aren’t Magic: A Practical Guide

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 FunctionsModel Selectionbias‑variance

0 likes · 6 min read

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

DataFunTalk

Sep 21, 2018 · Artificial Intelligence

Construction of a Second‑Hand E‑commerce Knowledge Graph and Its Application in Pricing Models

This article explains how a knowledge graph for second‑hand e‑commerce is built—from data extraction and entity, attribute, and relation mining to ontology construction, entity alignment, and graph integration—and describes how the resulting graph supports personalized recommendation, search optimization, and statistical or regression‑based pricing models.

NLPentity extractione‑commerce

0 likes · 15 min read

Construction of a Second‑Hand E‑commerce Knowledge Graph and Its Application in Pricing Models

Tencent Cloud Developer

Sep 20, 2018 · Industry Insights

How Big Data Drives Intelligent Outbound Calls and AI Customer Service

This article explains how a data‑driven platform combines big‑data preprocessing, behavior‑prediction models, and AI‑powered voice and text services to improve pre‑sale lead scoring, targeted SMS campaigns, and post‑sale customer support, using Tencent Cloud's TI One platform as a case study.

AI Customer ServiceBig DataIndustry Insights

0 likes · 17 min read

How Big Data Drives Intelligent Outbound Calls and AI Customer Service

Qunar Tech Salon

Sep 19, 2018 · Artificial Intelligence

Logistic Regression Tutorial with scikit-learn

This article introduces logistic regression, explains its theoretical basis, details key scikit-learn parameters, and provides a complete Python example for breast cancer classification, covering data preprocessing, model training, prediction, and evaluation with classification reports.

Pythonclassificationdata preprocessing

0 likes · 7 min read

Logistic Regression Tutorial with scikit-learn

Alibaba Cloud Developer

Sep 14, 2018 · Artificial Intelligence

How Alibaba’s UC Team Boosted Short‑Video Recommendations with FM+GBM

This article details the evolution of Alibaba's short‑video feed ranking system, from a Wide&Deep CTR model to a hybrid Factorization‑Machine and Gradient‑Boosted‑Tree approach, describing feature engineering, model architecture, experimental results, lessons learned, and future directions toward duration‑based relevance.

factorization machinesgradient boostingmachine learning

0 likes · 11 min read

How Alibaba’s UC Team Boosted Short‑Video Recommendations with FM+GBM

Alibaba Cloud Developer

Sep 12, 2018 · Artificial Intelligence

How Alibaba’s XSigma AI Engine Revolutionizes Customer Service Scheduling

The XSigma system combines AI‑driven demand forecasting, real‑time optimization, visual decision‑making and intelligent training to automatically schedule, scale, balance load and match customers with the best agents, dramatically improving resource utilization and user experience for Alibaba’s massive CCO operation.

Artificial IntelligenceOperationsScheduling

0 likes · 19 min read

How Alibaba’s XSigma AI Engine Revolutionizes Customer Service Scheduling

转转QA

Sep 12, 2018 · Information Security

Understanding Spam Prevention: Cheating Types and Anti‑Cheat Strategies in Zhuanzhuan's Risk Control System

The article explains Zhuanzhuan's risk‑control architecture, detailing content and behavior cheating types, three anti‑cheat approaches—strategy, product, and model—and practical interception, rule‑penalty mechanisms, and integration tips for developers and security engineers.

Information Securityanti-cheatmachine learning

0 likes · 9 min read

Understanding Spam Prevention: Cheating Types and Anti‑Cheat Strategies in Zhuanzhuan's Risk Control System

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-warehousefeature engineering

0 likes · 19 min read

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

Tencent Cloud Developer

Sep 10, 2018 · Artificial Intelligence

Machine Learning vs. Deep Learning: Differences, Applications, and Future Trends

The article explains that machine learning encompasses a range of algorithms such as decision trees and random forests, while deep learning—a specialized subset using multi‑layer neural networks—requires large data, powerful hardware, and longer training, yet offers superior performance in fields like computer vision, NLP, and medical diagnosis, and both are poised for expanding industrial and research adoption.

ApplicationsArtificial IntelligenceComparison

0 likes · 9 min read

Machine Learning vs. Deep Learning: Differences, Applications, and Future Trends

58 Tech

Sep 7, 2018 · Artificial Intelligence

Cupid Push Control System: Machine‑Learning‑Driven Notification Optimization at 58.com

The article details how 58.com’s Cupid push control system leverages machine‑learning models, especially XGBoost‑based CTR prediction, to prioritize and filter billions of daily push notifications, improving click‑through rates, reducing user annoyance, and providing a scalable, data‑driven architecture for diverse business services.

AB testingCTR predictionSystem Architecture

0 likes · 13 min read

Cupid Push Control System: Machine‑Learning‑Driven Notification Optimization at 58.com

Hulu Beijing

Sep 7, 2018 · Artificial Intelligence

From Chess Engines to AlphaGo Zero: The Evolution of Game AI

This article traces the history of game artificial intelligence—from early MiniMax chess programs and classic board‑game breakthroughs like Deep Blue, through AlphaGo’s triumph over human champions, to the self‑learning AlphaGo Zero—while explaining why games serve as a vital testbed for modern AI research.

AlphaGogame AImachine learning

0 likes · 13 min read

From Chess Engines to AlphaGo Zero: The Evolution of Game AI

JD Tech

Sep 7, 2018 · Information Security

Big Data and AI Security Insights from ISC 2018 Conference

The ISC 2018 conference highlighted the growing importance of big data and artificial intelligence security, presenting JD's research on anti‑scraping techniques, AI‑driven defenses against black‑market attacks, and a service‑oriented approach to protecting user data across enterprises.

AI securityBig DataInformation Security

0 likes · 5 min read

Big Data and AI Security Insights from ISC 2018 Conference

Meituan Technology Team

Sep 6, 2018 · Artificial Intelligence

Meituan Machine Learning Practice Book Released

Meituan’s new book, 'Machine Learning Practice,' authored by over twenty frontline engineers, offers a comprehensive guide to internet‑company ML techniques, is now sold in major bookstores, invites reader feedback, and provides QR‑code access to the team’s official account and an electronic bibliography, plus a companion summary of 27 essential AI articles.

AIMeituanalgorithm

0 likes · 6 min read

Meituan Machine Learning Practice Book Released

Big Data and Microservices

Sep 4, 2018 · Big Data

Exploring Five Big Data Architectures—from Traditional to Unified AI Designs

The article examines the evolution of big‑data processing by comparing five prevalent architectures—traditional Hadoop‑based stacks, streaming‑only designs, Kappa, Lambda, and the unified Unifield model—highlighting their strengths, weaknesses, and suitable scenarios while discussing the limitations of classic BI systems and the role of distributed storage, computation, and machine‑learning integration.

Big DataData ArchitectureHadoop

0 likes · 14 min read

Exploring Five Big Data Architectures—from Traditional to Unified AI Designs

Sohu Tech Products

Aug 29, 2018 · Artificial Intelligence

News Recommendation Algorithms: Architecture, Recall, and Ranking Techniques

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

collaborative filteringmachine learningnews recommendation

0 likes · 14 min read

News Recommendation Algorithms: Architecture, Recall, and Ranking Techniques

iQIYI Technical Product Team

Aug 24, 2018 · Artificial Intelligence

Lookalike Audience Extension Algorithms in iQIYI Advertising: Tag‑Based and Machine‑Learning Approaches

iQIYI uses two Lookalike audience extension methods—tag‑based using weighted tag scoring and supervised machine‑learning using logistic regression with engineered DMP and ad behavior features—both improving ad performance, e.g., 20% higher Trueview completion and up to 60% lower conversion cost.

AdvertisingAudience ExtensionLookalike

0 likes · 10 min read

Lookalike Audience Extension Algorithms in iQIYI Advertising: Tag‑Based and Machine‑Learning Approaches

Alibaba Cloud Developer

Aug 23, 2018 · Artificial Intelligence

Boost Short-Video Recommendations: Multi-Goal Optimization with Weighted Logistic Regression

Alibaba's short‑video recommendation team details how they enhance both click‑through rate and viewing duration by applying sample reweighting, weighted logistic regression, and playback‑completion‑rate normalization, achieving over 6% offline AUC gains and more than 10% increase in average user watch time.

click-through ratemachine learningmulti-objective optimization

0 likes · 10 min read

Boost Short-Video Recommendations: Multi-Goal Optimization with Weighted Logistic Regression

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.

ML pipelineModel Monitoringbest practices

0 likes · 35 min read

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

DataFunTalk

Aug 21, 2018 · Artificial Intelligence

iQIYI Traffic Anti-Cheat: Techniques, System Architecture, and Future Directions

This article provides a comprehensive overview of iQIYI's traffic anti‑cheat mechanisms, covering definitions of fraudulent traffic, industry challenges, data cleaning relationships, system design, rule‑based and machine‑learning solutions, feature engineering, model evaluation, monitoring, service applications, and future prospects.

Big DataSystem ArchitectureTraffic analysis

0 likes · 11 min read

iQIYI Traffic Anti-Cheat: Techniques, System Architecture, and Future Directions

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.

EngineeringModel Deploymentbest practices

0 likes · 41 min read

43 Essential Rules for Building Robust Machine Learning Systems

DataFunTalk

Aug 17, 2018 · Artificial Intelligence

Technical Evolution and Architecture of Shenma Search Engine

The article outlines Shenma Search's development history, its AI‑driven relevance and ranking technologies, the underlying system architecture based on Zookeeper and YARN, and discusses challenges in query understanding, machine‑learning ranking, and deep‑learning solutions for large‑scale search.

AINLPmachine learning

0 likes · 17 min read

Technical Evolution and Architecture of Shenma Search Engine

Alibaba Cloud Developer

Aug 8, 2018 · Artificial Intelligence

How Alibaba’s AI Prediction Platform Boosts Smart Customer Service

The article describes Alibaba’s AI‑driven prediction platform for its smart‑customer‑service bots, detailing background, order and issue prediction capabilities, deployed products, underlying algorithms such as DeepFM, DCN, reinforcement learning, streaming computation, and the platform’s modular architecture that enables scalable, automated model management.

AIDeep Learningmachine learning

0 likes · 13 min read

How Alibaba’s AI Prediction Platform Boosts Smart Customer Service

58 Tech

Aug 8, 2018 · Databases

58 Cloud DB Platform: Architecture, Automation, and Intelligent Operations

The article presents a detailed case study of the 58 Cloud DB Platform, describing its architecture, automated workflow using Celery and Ansible, and intelligent features such as server selection and alarm merging powered by machine‑learning, highlighting how it streamlines MySQL, Redis, and MongoDB operations for developers and DBAs.

MongoDBMySQLOperations

0 likes · 10 min read

58 Cloud DB Platform: Architecture, Automation, and Intelligent Operations

Ctrip Technology

Aug 7, 2018 · Artificial Intelligence

AI‑Driven Intelligent Customer Service at Ctrip: Algorithms and Practices

This article describes how Ctrip leverages machine‑learning and deep‑learning techniques—such as question‑answer matching, context‑aware dialogue models, and input‑suggestion algorithms—to automate repetitive customer‑service tasks, improve response efficiency, and enhance user experience across its travel platform.

AIChatbotmachine learning

0 likes · 13 min read

AI‑Driven Intelligent Customer Service at Ctrip: Algorithms and Practices

Tencent Cloud Developer

Aug 3, 2018 · Artificial Intelligence

Analysis of Google Quickdraw CNN‑RNN Model for Sketch Recognition

The article dissects Google’s Quickdraw sketch‑recognition model, detailing its 1‑D convolutional front‑end, Bi‑LSTM encoder, and softmax classifier, explaining the TFRecord‑based normalization and interpolation steps, why pooling harms accuracy, and how the massive dataset can fuel diverse sequential‑learning applications and product concepts.

CNNRNNSketch Recognition

0 likes · 7 min read

Analysis of Google Quickdraw CNN‑RNN Model for Sketch Recognition

Practical DevOps Architecture

Aug 2, 2018 · Artificial Intelligence

Naive Bayes Text Classification: Theory, Implementation, and Evaluation

This article explains the principles of Naive Bayes text classification, detailing feature representation, model selection, training and testing procedures, probability calculations, code implementation in Python, and evaluation metrics such as accuracy, precision, recall, PR and ROC curves.

Naive BayesPythonevaluation metrics

0 likes · 22 min read

Naive Bayes Text Classification: Theory, Implementation, and Evaluation

Tencent Cloud Developer

Aug 1, 2018 · Artificial Intelligence

How AI Powers Real-World Apps: From Face Filters to Medical Imaging

The July 28 Tencent Cloud community salon in Beijing gathered five AI experts who demonstrated practical AI applications—including computer‑vision face filters, OCR services, smart construction attendance, game AI, and breast‑cancer detection—showing how cloud‑based models, data pipelines, and deployment strategies turn research into usable products.

AICloud AIComputer Vision

0 likes · 21 min read

How AI Powers Real-World Apps: From Face Filters to Medical Imaging

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

Bitu Technology

Jul 19, 2018 · Artificial Intelligence

Introduction to Deep Learning: Concepts, Examples, and Learning Resources

This article provides a comprehensive overview of deep learning, covering its definition, fundamental machine‑learning components, illustrative examples such as hot‑dog classification and house‑price prediction, the mathematics of cost functions and gradient descent, back‑propagation via the chain rule, and practical resources and code snippets using Torch.

BackpropagationCode ExamplesNeural Networks

0 likes · 11 min read

Introduction to Deep Learning: Concepts, Examples, and Learning Resources

Alibaba Cloud Developer

Jul 18, 2018 · Artificial Intelligence

How Alibaba’s Brand‑Level Ranking Boosts E‑Commerce Clicks with Attention‑GRU

This paper presents Alibaba's first brand‑level ranking system that personalizes brand ordering on e‑commerce platforms by designing brand features and extending an Attention‑GRU model with three key improvements, demonstrating significant offline and online performance gains on the Tmall marketplace.

attention GRUbrand rankinge-commerce recommendation

0 likes · 27 min read

Tencent Cloud Developer

Jul 13, 2018 · Artificial Intelligence

Using Facebook Prophet for Time Series Forecasting: Predicting Tencent Cloud Database Storage Trends

The article explains Facebook Prophet’s additive regression model and demonstrates its use to forecast Tencent Cloud database storage demand, showing upward trends and growing uncertainty from January‑June 2018 data, while highlighting practical applications for internal customer identification and capacity planning.

Additive Regression ModelData ScienceDatabase Storage Prediction

0 likes · 5 min read

Using Facebook Prophet for Time Series Forecasting: Predicting Tencent Cloud Database Storage Trends

High Availability Architecture

Jul 12, 2018 · Information Security

Evolution of Zhihu’s Anti‑Cheat System “Wukong”: Architecture, Strategies, and Lessons Learned

This article chronicles the three‑generation evolution of Zhihu’s anti‑cheat platform Wukong, detailing its business context, spam taxonomy, multi‑layered control methods, architectural redesigns, strategy language improvements, graph‑based risk analysis, and the continuous integration of big‑data and machine‑learning techniques to combat content and behavior spam.

Big DataInformation SecurityRisk management

0 likes · 23 min read

Evolution of Zhihu’s Anti‑Cheat System “Wukong”: Architecture, Strategies, and Lessons Learned

Architecture Digest

Jul 12, 2018 · Artificial Intelligence

How to Choose the Right Machine Learning Algorithm

This article explains that there is no universal solution for selecting machine learning algorithms and outlines practical factors—such as data characteristics, problem type, business constraints, and algorithm complexity—to help practitioners systematically narrow down and pick the most suitable models.

Model Evaluationalgorithm selectiondata preprocessing

0 likes · 14 min read

How to Choose the Right Machine Learning Algorithm

Alibaba Cloud Developer

Jul 11, 2018 · Artificial Intelligence

Can Global Ranking Boost E‑Commerce GMV? A New AI Approach

Traditional e‑commerce ranking ignores interactions among displayed items, but this study introduces a novel global ranking method that models mutual influences, optimizes expected GMV using extended global features and RNN‑based sequence generation, achieving a 5% GMV lift in large‑scale A/B tests.

Attention MechanismGMVRNN

0 likes · 12 min read

Can Global Ranking Boost E‑Commerce GMV? A New AI Approach

Architects' Tech Alliance

Jul 11, 2018 · Artificial Intelligence

AI Technology Trends and Reference Architecture Overview

The article reviews the evolution of artificial intelligence, presents a comprehensive AI reference framework based on roles, activities and functions, explains the intelligent information chain and IT value chain, and details current AI technology trends such as machine learning, deep learning, transfer learning, active learning and evolutionary learning, while also noting talent shortages and promoting an AI education course.

AI trendsDeep Learningmachine learning

0 likes · 13 min read

AI Technology Trends and Reference Architecture Overview

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.

Model Managementfeature engineeringmachine learning

0 likes · 15 min read

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

Tencent Cloud Developer

Jul 6, 2018 · Big Data

Big Data Book List

In the era of big data, this curated list highlights essential print titles—from machine learning and statistical learning to Hadoop, predictive analytics, data visualization, and data engineering—offering readers a comprehensive roadmap to deepen practical knowledge and stay current with rapidly evolving technologies.

AIBooksData Science

0 likes · 8 min read

ITFLY8 Architecture Home

Jul 2, 2018 · Artificial Intelligence

How JD.com Built a Multi‑Screen Personalized Recommendation Engine

This article explains how JD.com evolved its recommendation system from simple product suggestions to a sophisticated, multi‑screen, multi‑type personalized engine using big‑data collection, real‑time behavior tracking, machine‑learning models, and a modular architecture that boosts conversion and user experience.

Big Datae‑commercemachine learning

0 likes · 14 min read

How JD.com Built a Multi‑Screen Personalized Recommendation Engine

DataFunTalk

Jul 2, 2018 · Artificial Intelligence

Overview of Sogou Information Feed Recommendation Algorithms

This article summarizes Sogou's information‑feed recommendation system, covering the architecture from data collection and NLP processing to recall, ranking, and feedback, and detailing the classification, tagging, keyword extraction, and various recall and ranking models such as FastText, TextCNN, collaborative filtering, and wide‑and‑deep learning.

NLPSogouinformation feed

0 likes · 14 min read

Overview of Sogou Information Feed Recommendation Algorithms

Architecture Digest

Jul 1, 2018 · Artificial Intelligence

Evolution and Architecture of JD.com Recommendation System

The article outlines the development, multi‑screen deployment, system architecture, data platform, and core recommendation engine of JD.com’s e‑commerce recommendation platform, highlighting how big‑data and AI techniques enable personalized product, activity, and content suggestions across various user touchpoints.

e‑commercemachine learningpersonalization

0 likes · 16 min read

Evolution and Architecture of JD.com Recommendation System

Alibaba Cloud Developer

Jun 29, 2018 · Artificial Intelligence

How AI Powers Heterogeneous Content Ranking in E‑Commerce Search

This paper addresses the challenge of ranking heterogeneous data in e‑commerce by proposing two algorithms—a multi‑armed bandit approach and a personalized Markov deep neural network—to select and order content streams, demonstrating superior performance over baseline models in A/B tests.

Bandit AlgorithmsDeep Learningcontent ranking

0 likes · 7 min read

How AI Powers Heterogeneous Content Ranking in E‑Commerce Search

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.

Online Learningcold startcontent diversity

0 likes · 17 min read

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

MaGe Linux Operations

Jun 22, 2018 · Artificial Intelligence

8 Fast Python Linear Regression Techniques Compared for Speed and Complexity

This article reviews eight Python-based simple linear regression methods, explains their underlying algorithms, compares their computational complexity and execution speed on datasets up to ten million points, and offers guidance on selecting the most efficient approach for data‑science tasks.

NumPylinear regressionmachine learning

0 likes · 10 min read

8 Fast Python Linear Regression Techniques Compared for Speed and Complexity

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.

Deep LearningModel EvaluationUnsupervised Learning

0 likes · 8 min read

Essential Machine Learning Algorithms Every Beginner Must Know

Alibaba Cloud Developer

Jun 20, 2018 · Artificial Intelligence

How Dynamic Fusion Improves Life‑Stage Prediction for Baby Products Using AI

This article presents a machine‑learning approach that dynamically fuses monthly probability distributions to infer infants' life stages from parents' e‑commerce behavior, demonstrating higher accuracy and recall than memory‑less baselines across sparse and dense data periods.

dynamic fusione‑commercefeature engineering

0 likes · 13 min read

How Dynamic Fusion Improves Life‑Stage Prediction for Baby Products Using AI

Ctrip Technology

Jun 19, 2018 · Artificial Intelligence

AIOps at Ctrip: Concepts, Typical Application Scenarios, and Algorithmic Practices

This article introduces Ctrip's AIOps journey, explaining the AI‑driven operations concept, showcasing typical use cases such as anomaly detection, intelligent fault diagnosis, and resource utilization improvement, and detailing the underlying statistical and machine‑learning algorithms that enable these capabilities.

CtripOperationsResource Optimization

0 likes · 16 min read

AIOps at Ctrip: Concepts, Typical Application Scenarios, and Algorithmic Practices

ITPUB

Jun 19, 2018 · Databases

Can Machine Learning Replace Traditional Hashing? A Deep Dive into Learned Indexes

This article explores the evolution of indexing from classic hash tables and B‑Trees to learned index structures, explaining hash functions, collision handling, machine‑learning fundamentals, and the promise and limits of using ML models to improve memory efficiency and query performance.

Hashingcuckoo hashinglearned indexes

0 likes · 24 min read

Can Machine Learning Replace Traditional Hashing? A Deep Dive into Learned Indexes

Qunar Tech Salon

Jun 15, 2018 · Artificial Intelligence

Predicting the 2018 FIFA World Cup Winners Using Machine Learning

This article demonstrates how to collect historical football data, perform exploratory analysis and feature engineering, and apply a logistic‑regression model in Python to predict the 2018 FIFA World Cup champion, group‑stage results, and knockout‑stage outcomes.

FIFA World CupPythondata analysis

0 likes · 8 min read

Predicting the 2018 FIFA World Cup Winners Using Machine Learning

21CTO

Jun 14, 2018 · Artificial Intelligence

What Data Scientists Chose in 2018: Top AI, ML, and Big Data Tools Revealed

The 2018 KDnuggets survey of over 2,000 data‑science professionals shows Python dominating with 66% usage, R dropping below 50%, TensorFlow leading deep‑learning frameworks, RapidMiner gaining traction, SQL remaining stable, Hadoop declining, and regional participation shifting toward Europe.

Data ScienceDeep LearningPython

0 likes · 9 min read

What Data Scientists Chose in 2018: Top AI, ML, and Big Data Tools Revealed

AntTech

Jun 14, 2018 · Artificial Intelligence

A Local Online Learning Approach for Non-linear Data (SCW-LOL)

This paper introduces the SCW-LOL algorithm, a local online learning method based on Soft Confidence Weighted that extends a global model with multiple local classifiers, uses online K‑Means for sample assignment, provides theoretical error bounds, and demonstrates superior performance on ten benchmark datasets, especially for multi‑class classification.

Online LearningSCW algorithmdata mining

0 likes · 9 min read

A Local Online Learning Approach for Non-linear Data (SCW-LOL)

Meitu Technology

Jun 13, 2018 · Artificial Intelligence

Meipai AI Tech Talk: Deep Ranking Models, Video Clustering, and Optimization

The talk covered Meipai’s personalized deep ranking model that balances depth and low latency, a behavior‑driven video clustering method that enriches recommendation beyond visual cues, and the use of advanced data structures to accelerate solving large‑scale optimization problems in business contexts.

Neural Networksmachine learningoptimization

0 likes · 5 min read

Meipai AI Tech Talk: Deep Ranking Models, Video Clustering, and Optimization

DataFunTalk

Jun 13, 2018 · Artificial Intelligence

Evolution of E‑commerce Platform Recommendation System Architecture

This article reviews the evolution of recommendation system architecture for C2C e‑commerce platforms, tracing stages from simple offline‑online pipelines through granular feed‑flow improvements, real‑time processing, and machine‑learning‑driven models, while highlighting user‑profile construction, challenges, and best‑practice guidelines.

ArchitectureReal-Timee‑commerce

0 likes · 10 min read

Evolution of E‑commerce Platform Recommendation System Architecture

Alibaba Cloud Developer

Jun 13, 2018 · Artificial Intelligence

How Alibaba’s Knowledge Graph Powers Real-Time Product Safety with AI

Alibaba’s knowledge graph leverages massive product data, NLP, semantic reasoning, and machine‑learning inference to detect and block counterfeit, infringing, or unsafe items in real time, providing millisecond‑level responses, self‑learning capabilities, and explainable decisions across e‑commerce platforms, thereby protecting intellectual property and consumer rights.

AIe‑commerceknowledge graph

0 likes · 9 min read

How Alibaba’s Knowledge Graph Powers Real-Time Product Safety with AI

Alibaba Cloud Developer

Jun 13, 2018 · Artificial Intelligence

What Machine Learning Can Teach Us About Growing Up

Using a stroll conversation among Ant Financial AI team members, the article likens machine learning concepts such as overfitting, generalization, supervised and unsupervised learning, transfer learning, and model interpretability to human development stages, illustrating how both require diverse data, training, and evolving algorithms.

AI educationGeneralizationhuman development

0 likes · 10 min read

What Machine Learning Can Teach Us About Growing Up

Hulu Beijing

Jun 8, 2018 · Artificial Intelligence

How Hulu Leverages AI for Video Recommendation, Content Understanding, and Ads

The article reviews Hulu’s 2018 iQIYI keynote on AI video applications, detailing how AI drives personalized recommendations, content analysis through computer vision and NLP, ad targeting across visual, linguistic, and semantic layers, and outlines the platform’s machine‑learning architecture and future directions.

AIHulucontent understanding

0 likes · 6 min read

How Hulu Leverages AI for Video Recommendation, Content Understanding, and Ads

ITFLY8 Architecture Home

Jun 7, 2018 · Artificial Intelligence

How Modern Recommendation Systems Work: Architecture, Algorithms, and Best Practices

This article explains the goals, architectures, data pipelines, recall strategies, and ranking models of contemporary recommendation systems, covering both online and offline components, collaborative filtering, content-based methods, feature engineering, and practical interview insights for engineers.

Ranking Modelscollaborative filteringmachine learning

0 likes · 18 min read

How Modern Recommendation Systems Work: Architecture, Algorithms, and Best Practices