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1918 articles · Page 19 of 20
Meitu Technology
Meitu Technology
Apr 6, 2017 · Artificial Intelligence

Meipai Text Matters: Mining and Practice of Community Text

The talk demonstrates how Meipai, a leading Chinese short‑video community, leverages large‑scale text mining and machine‑learning techniques—ranging from anti‑spam filtering to AI‑enhanced search—to enrich captions, comments, and metadata, improve user experience, and inspire further research on text data in video platforms.

Community analysisSearch Engineanti-spam
0 likes · 2 min read
Meipai Text Matters: Mining and Practice of Community Text
Meitu Technology
Meitu Technology
Apr 6, 2017 · Artificial Intelligence

Meitu Internet Technology Salon: AI and Machine Learning Applications in Practice

The fourth Meitu Internet Technology Salon showcased practical AI and machine learning uses, highlighting Meipai’s text‑anti‑spam, hot‑topic detection, sentiment analysis and personalized video search, while Baidu demonstrated ML‑driven business intelligence tools for multi‑source data mining, user profiling, and intelligent enterprise and HR management.

Business IntelligenceSearch EngineSentiment Analysis
0 likes · 7 min read
Meitu Internet Technology Salon: AI and Machine Learning Applications in Practice
21CTO
21CTO
Apr 4, 2017 · Artificial Intelligence

How Vipshop Evolved Its Real-Time Personalized Recommendation Engine

This article recounts Wu Guanlin’s presentation on the evolution of Vipshop’s personalized recommendation system, detailing the technical challenges of real‑time predictions, the three generations of architecture, the four‑stage recommendation engine, and the VRE platform’s design for scalability and low latency.

Big Datamachine learningreal-time prediction
0 likes · 10 min read
How Vipshop Evolved Its Real-Time Personalized Recommendation Engine
21CTO
21CTO
Apr 1, 2017 · Artificial Intelligence

How Modern Apps Use AI to Personalize Your Content Feed

The article explores how recommendation technologies powered by machine learning permeate everyday platforms—from e‑commerce and video services to social media and news apps—detailing the data they collect, the algorithms they employ, and the limits of personalization in unpredictable human scenarios.

Recommendation Systemscontent filteringmachine learning
0 likes · 7 min read
How Modern Apps Use AI to Personalize Your Content Feed
Meituan Technology Team
Meituan Technology Team
Mar 24, 2017 · Artificial Intelligence

Tourism Recommendation System: Strategy Iterations, Architecture, and Future Challenges

The article outlines Meituan‑Dianping’s tourism recommendation system, detailing its evolution from simple hot‑sale recall to sophisticated decay‑based, GPS‑aware, collaborative filtering and XGBoost reranking pipelines, the four‑layer architecture supporting dozens of travel scenarios, and future plans to broaden recall, adopt deep models, and expand multimodal travel recommendations.

Big DataRankingTourism
0 likes · 26 min read
Tourism Recommendation System: Strategy Iterations, Architecture, and Future Challenges
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 16, 2017 · Artificial Intelligence

How Alibaba Harnesses Deep Reinforcement Learning for E‑Commerce Innovation

This interview with Alibaba researcher Xu Yinghui reveals how the company built large‑scale deep reinforcement learning systems for search, recommendation, logistics and online advertising, detailing team structures, technical breakthroughs, training challenges, and future directions such as multi‑agent learning and GAN integration.

AIAlibabaOnline Advertising
0 likes · 20 min read
How Alibaba Harnesses Deep Reinforcement Learning for E‑Commerce Innovation
Qunar Tech Salon
Qunar Tech Salon
Mar 12, 2017 · Big Data

Essential Skills and Career Paths for Data Professionals: From Big Data Platforms to AI

The article outlines the key competencies, responsibilities, and career development advice for data professionals across the entire data stack—from building big‑data platforms and data warehouses to visualization, analysis, algorithm engineering, and deep‑learning applications—emphasizing the importance of creating business value with data.

Big DataData AnalystData Engineering
0 likes · 15 min read
Essential Skills and Career Paths for Data Professionals: From Big Data Platforms to AI
MaGe Linux Operations
MaGe Linux Operations
Mar 3, 2017 · Artificial Intelligence

Top 5 Python Libraries to Supercharge Your Machine Learning Projects

This article introduces five highly rated Python libraries—PyWren, Tfdeploy, Luigi, Kubelib, and PyTorch—that streamline data handling, cloud execution, workflow orchestration, and GPU acceleration, helping machine‑learning engineers boost productivity and tackle complex projects more efficiently.

AWS LambdaPyTorchPython
0 likes · 6 min read
Top 5 Python Libraries to Supercharge Your Machine Learning Projects
21CTO
21CTO
Mar 2, 2017 · Artificial Intelligence

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

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

Recommendation Systemse-commercefeature engineering
0 likes · 13 min read
How User Personas Power Modern Recommendation Systems: From Theory to NetEase Yanxuan
Nightwalker Tech
Nightwalker Tech
Mar 2, 2017 · Information Security

Techniques and Tools for Anti‑Spam Content Filtering in PHP

The discussion outlines practical anti‑spam strategies—including text length limits, keyword replacement, trie‑based data structures, AC automata, Bayesian and vector‑similarity algorithms, and PHP extensions such as libdatrie—while also sharing performance metrics and resource links for implementing robust content filtering systems.

PHPTriecontent filtering
0 likes · 4 min read
Techniques and Tools for Anti‑Spam Content Filtering in PHP
MaGe Linux Operations
MaGe Linux Operations
Feb 28, 2017 · Artificial Intelligence

Top 16 Python Machine Learning Libraries You Should Know

This article provides a concise overview of sixteen popular Python machine‑learning libraries—including scikit‑learn, NLTK, Theano, and Orange—detailing their main features, typical use cases, and where to find their project pages, making it a handy reference for data‑science practitioners.

Pythonartificial-intelligencedata science
0 likes · 14 min read
Top 16 Python Machine Learning Libraries You Should Know
Nightwalker Tech
Nightwalker Tech
Feb 27, 2017 · Big Data

Community Discussion on Learning Paths, Tools, and Applications in Big Data

A diverse group of practitioners share recommendations for books, technologies, real‑world use cases, and practical challenges when learning and applying big‑data processing, covering Hadoop, Spark, data visualization, ETL, and the relationship between data, algorithms, and business value.

Big DataHadoopdata analysis
0 likes · 16 min read
Community Discussion on Learning Paths, Tools, and Applications in Big Data
Alibaba Cloud Developer
Alibaba Cloud Developer
Feb 27, 2017 · Artificial Intelligence

Essential Machine Learning Algorithms Every Beginner Must Know

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

ensemble methodsfeature engineeringmachine learning
0 likes · 9 min read
Essential Machine Learning Algorithms Every Beginner Must Know
Alibaba Cloud Developer
Alibaba Cloud Developer
Feb 24, 2017 · Artificial Intelligence

Alibaba’s Reinforcement Learning Boost for E‑Commerce Search & Recommendations

Alibaba leveraged reinforcement learning, highlighted by MIT Technology Review’s 2017 breakthrough list, to transform its e‑commerce search and recommendation systems during Double 11, deploying large‑scale online and batch training pipelines, dynamic market segmentation, and real‑time decision models that boosted click‑through rates by up to 20 %.

e-commercemachine learningonline training
0 likes · 14 min read
Alibaba’s Reinforcement Learning Boost for E‑Commerce Search & Recommendations
Alibaba Cloud Developer
Alibaba Cloud Developer
Feb 20, 2017 · Artificial Intelligence

How Alibaba’s Graph Embedding Boosts E‑Commerce Recommendations by 60%

Alibaba’s merchant division introduced a scalable graph‑embedding approach for its “thousands‑of‑people‑one‑face” recommendation module, enabling personalized product suggestions within sparse shop data, improving click‑through rates by 30% and conversions by 60%, and presenting theoretical insights validated at AAAI 2017.

e-commercegraph embeddingmachine learning
0 likes · 13 min read
How Alibaba’s Graph Embedding Boosts E‑Commerce Recommendations by 60%
Meituan Technology Team
Meituan Technology Team
Feb 17, 2017 · Big Data

User Profiling and Machine Learning Practices for Food Delivery O2O Platforms

Meituan Delivery’s rapid expansion across multiple categories relies on detailed user profiling and machine‑learning models—such as high‑potential customer prediction, churn risk regression and Cox survival analysis—to personalize acquisition, retention, and scenario‑based cross‑selling, while addressing sparse behavior, unstructured data, and geographic context challenges.

Big DataO2Ochurn prediction
0 likes · 13 min read
User Profiling and Machine Learning Practices for Food Delivery O2O Platforms
Qunar Tech Salon
Qunar Tech Salon
Jan 24, 2017 · Artificial Intelligence

Practical Approaches to Deploying Machine Learning Models: Real‑time SOA, PMML, Rserve, and Spark

This article shares practical engineering experiences for deploying machine learning models in various scenarios—real‑time low‑volume predictions via Rserve or Python‑httpserve, high‑throughput real‑time serving with PMML‑wrapped Java classes, and offline batch predictions using simple shell scripts—detailing tools, performance considerations, and implementation steps.

Model DeploymentPMMLPython
0 likes · 11 min read
Practical Approaches to Deploying Machine Learning Models: Real‑time SOA, PMML, Rserve, and Spark
Ctrip Technology
Ctrip Technology
Jan 22, 2017 · Artificial Intelligence

Cross-Domain Recommendation: Concepts, Methods, and Novel Approaches

This article reviews the fundamentals of cross-domain recommendation, explains the limitations of single‑domain personalized recommendation, surveys existing collaborative‑filtering, transfer‑learning, and knowledge‑based methods, and introduces two novel tensor‑factorization and bilinear multilevel models that achieve superior performance on real datasets.

collaborative filteringcross-domain recommendationknowledge-based recommendation
0 likes · 17 min read
Cross-Domain Recommendation: Concepts, Methods, and Novel Approaches
Alibaba Cloud Developer
Alibaba Cloud Developer
Jan 20, 2017 · Artificial Intelligence

How E‑Commerce Giants Leverage Recommendation Algorithms – Insights from Xavier Amatriain

An illustrated guide explores the recommendation algorithms powering e‑commerce platforms, drawing on Xavier Amatriain’s CMU Machine Learning summer school lectures to explain collaborative filtering, content‑based, and hybrid approaches, their practical implementations, and the impact on user experience and sales.

Xavier Amatriaincollaborative filteringe-commerce
0 likes · 4 min read
How E‑Commerce Giants Leverage Recommendation Algorithms – Insights from Xavier Amatriain
Ctrip Technology
Ctrip Technology
Jan 5, 2017 · Artificial Intelligence

Design and Implementation of a Billion‑Scale Generalized Recommendation System at Tencent Cloud

This article explains how Tencent built a billion‑scale, generalized recommendation system by designing a reusable algorithm library, deploying a low‑latency, highly available real‑time streaming platform (R2), and offering a cloud‑based recommendation engine that simplifies integration for internet businesses.

AICloud ComputingLarge Scale
0 likes · 11 min read
Design and Implementation of a Billion‑Scale Generalized Recommendation System at Tencent Cloud
Ctrip Technology
Ctrip Technology
Jan 5, 2017 · Artificial Intelligence

Practical Approaches to Deploying Machine Learning Models: PMML, Rserve, and Spark in Production

This article shares practical engineering experiences for deploying machine learning models in production, covering three typical scenarios—real‑time small data, real‑time large data, and offline predictions—and detailing how to use PMML, Rserve, Spark, shell scripts, and related tools to meet performance and operational requirements.

Model DeploymentPMMLRserve
0 likes · 12 min read
Practical Approaches to Deploying Machine Learning Models: PMML, Rserve, and Spark in Production
Hulu Beijing
Hulu Beijing
Dec 21, 2016 · Artificial Intelligence

Inside NIPS 2016: Highlights, Papers, and Insights from Hulu’s Researchers

The article offers a comprehensive overview of the 2016 NIPS conference in Barcelona, detailing its history, attendance, Hulu’s contributions as presenters and reviewers, key tutorials, invited talks, award-winning papers, symposium highlights, and the broader impact of deep learning and AI advancements.

AI ConferenceBest PapersNeurIPS
0 likes · 12 min read
Inside NIPS 2016: Highlights, Papers, and Insights from Hulu’s Researchers
Alibaba Cloud Infrastructure
Alibaba Cloud Infrastructure
Dec 17, 2016 · Artificial Intelligence

Understanding Voiceprint Recognition: Principles, Techniques, and Applications

The article explains voiceprint (speaker) recognition technology, covering its biological basis, 1:1 verification versus 1:N identification, content‑related versus content‑independent approaches, key acoustic features such as MFCC, the iVector framework, system workflow diagrams, and its use in an Alibaba security challenge.

Biometricsmachine learningspeaker verification
0 likes · 10 min read
Understanding Voiceprint Recognition: Principles, Techniques, and Applications
High Availability Architecture
High Availability Architecture
Dec 11, 2016 · Artificial Intelligence

Why Machine Learning Is Hard: Debugging Challenges and Exponential Difficulty

The article explains that while machine learning has advanced with abundant courses, textbooks, and frameworks, engineers still face hard debugging problems due to algorithmic, implementation, data, and model dimensions, leading to exponential difficulty and long feedback loops that demand intuition and systematic testing.

Model Trainingartificial-intelligencedebugging
0 likes · 8 min read
Why Machine Learning Is Hard: Debugging Challenges and Exponential Difficulty
Meituan Technology Team
Meituan Technology Team
Dec 9, 2016 · Artificial Intelligence

A General Feature Production Framework for Meituan Delivery Ranking System

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

KV storefeature engineeringmachine learning
0 likes · 12 min read
A General Feature Production Framework for Meituan Delivery Ranking System
Alibaba Cloud Developer
Alibaba Cloud Developer
Dec 8, 2016 · Artificial Intelligence

How AI Powers Data‑Driven Merchant Success

In this Alibaba Tech Forum talk, senior expert Wei Hu explains how machine learning and big‑data technologies are used to empower merchants with personalized storefronts, intelligent posters, and AI‑driven headlines, boosting their efficiency and sales performance.

AIAlibabaBig Data
0 likes · 2 min read
How AI Powers Data‑Driven Merchant Success
Alibaba Cloud Developer
Alibaba Cloud Developer
Dec 7, 2016 · Artificial Intelligence

How Online AI Transforms Search and Recommendation Systems

At Alibaba's 2016 Double 11 Tech Forum, researcher Xu Yinghui presented how online AI technologies enhance search and recommendation on the e‑commerce platform, turning massive user behavior data into actionable insights that improve traffic allocation and maximize welfare for consumers, sellers, and the platform.

AIAlibabaSearch
0 likes · 2 min read
How Online AI Transforms Search and Recommendation Systems
Ctrip Technology
Ctrip Technology
Dec 2, 2016 · Big Data

Design and Architecture of Ctrip's Aegis Risk Control System

This article presents a comprehensive overview of Ctrip's Aegis risk control system, detailing its modular architecture, rule engine, data service layer, Chloro analytics platform, and future directions, while highlighting the use of streaming, big‑data processing, and machine‑learning models for real‑time fraud detection.

Big DataReal-time ProcessingRule Engine
0 likes · 13 min read
Design and Architecture of Ctrip's Aegis Risk Control System
Architects' Tech Alliance
Architects' Tech Alliance
Nov 24, 2016 · Big Data

Data Mining Overview: Process, Techniques, and Model Evaluation

This article provides a comprehensive introduction to data mining, covering its definition, goal setting, data sampling, exploration, preprocessing, pattern discovery, model building, evaluation methods, and the main analytical techniques such as classification, regression, clustering, association rules, feature and deviation analysis, and web mining.

Clusteringassociation rulesclassification
0 likes · 10 min read
Data Mining Overview: Process, Techniques, and Model Evaluation
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Nov 23, 2016 · Artificial Intelligence

How to Progress from Beginner to Expert in Machine Learning: A Four‑Stage Roadmap

This article outlines a four‑stage learning pathway for programmers—from initial exposure to advanced mastery—detailing the goals, recommended resources, and practical activities for each phase, helping readers identify their current level and plan concrete steps toward becoming proficient in machine learning.

AI EducationBeginner Guidecareer development
0 likes · 11 min read
How to Progress from Beginner to Expert in Machine Learning: A Four‑Stage Roadmap
21CTO
21CTO
Nov 6, 2016 · Artificial Intelligence

How to Build a Scalable AI-Powered Recommendation System with SOA

This article outlines a service‑oriented architecture for a high‑availability personalized recommendation platform, detailing the front‑end, back‑end, crawler, user‑profile modeling, data collection from logs and client events, and processing pipelines using technologies such as Node.js, Python, RabbitMQ/Kafka, MongoDB and TensorFlow.

Full-StackSOATensorFlow
0 likes · 5 min read
How to Build a Scalable AI-Powered Recommendation System with SOA
StarRing Big Data Open Lab
StarRing Big Data Open Lab
Nov 4, 2016 · Artificial Intelligence

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

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

Recommendation SystemsStandardizationclassification
0 likes · 20 min read
How Item Features Power Music Recommendations: A Hands‑On Guide
StarRing Big Data Open Lab
StarRing Big Data Open Lab
Oct 20, 2016 · Artificial Intelligence

How Collaborative Filtering Powers Recommendations: From Manhattan to Cosine Similarity

This article walks through the fundamentals of recommendation systems, explaining collaborative filtering and various similarity measures—including Manhattan, Euclidean, Minkowski, Pearson correlation, and cosine similarity—while discussing their suitability for dense, sparse, or biased rating data and introducing K‑Nearest Neighbors for practical implementation.

Recommendation Systemscollaborative filteringdata mining
0 likes · 15 min read
How Collaborative Filtering Powers Recommendations: From Manhattan to Cosine Similarity
Qunar Tech Salon
Qunar Tech Salon
Oct 17, 2016 · Information Security

Design and Implementation of a Cloud‑Based Web Application Firewall at Ctrip

This article describes Ctrip's challenges with web security, evaluates hardware and commercial cloud WAF shortcomings, and presents a low‑cost, low‑risk cloud‑based WAF solution that leverages DNS redirection, closed‑loop rule management, Lua/Tengine deployment, supervised machine‑learning log analysis, and big‑data streaming for real‑time threat detection and mitigation.

Big DataWAFcloud security
0 likes · 9 min read
Design and Implementation of a Cloud‑Based Web Application Firewall at Ctrip
Alibaba Cloud Developer
Alibaba Cloud Developer
Oct 8, 2016 · Artificial Intelligence

Unlocking Machine Learning Basics: From Perceptrons to Ensemble Models

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

Decision Treesensemble methodsfeature engineering
0 likes · 8 min read
Unlocking Machine Learning Basics: From Perceptrons to Ensemble Models
Alibaba Cloud Developer
Alibaba Cloud Developer
Sep 26, 2016 · Artificial Intelligence

Can Machine Learning Predict China’s Car License Lottery? Secrets in 13‑Digit IDs

This article investigates whether the 13‑digit user IDs used in Chinese car‑license lotteries are truly random, revealing how the ID generation, seed‑based selection, and hidden patterns—especially the influential seventh digit—affect outcomes, and demonstrates that simple linear models can achieve an AUC of around 0.8 in predicting winners, while also discussing the system’s opacity across major cities.

ID Generationcar license lotterydata analysis
0 likes · 17 min read
Can Machine Learning Predict China’s Car License Lottery? Secrets in 13‑Digit IDs
ITPUB
ITPUB
Sep 21, 2016 · Artificial Intelligence

Deep Learning Platforms Unveiled: From DistBelief to TensorFlow and Real‑World Uses

The article reviews the evolution and challenges of deep learning, outlines major commercial platforms such as DistBelief, COTS, and Adam, compares open‑source frameworks like MXNet, TensorFlow and Petuum, and highlights their architectures, performance metrics, and diverse applications ranging from image recognition to recommendation systems.

AIMXNetTensorFlow
0 likes · 11 min read
Deep Learning Platforms Unveiled: From DistBelief to TensorFlow and Real‑World Uses
Ctrip Technology
Ctrip Technology
Sep 19, 2016 · Artificial Intelligence

Personalized Demand Prediction and Ranking for Qunar's "Guess You Like" Feature

This article describes Qunar's personalized demand prediction system for the "Guess You Like" card, detailing how user‑demand associations are mined via rule engines, collaborative filtering, LBS and manual rules, and how ranking models evolve from subjective Bayes to RankBoost and LambdaMart, with experimental evaluation and future LSTM plans.

AIRankingTravel
0 likes · 10 min read
Personalized Demand Prediction and Ranking for Qunar's "Guess You Like" Feature
360 Zhihui Cloud Developer
360 Zhihui Cloud Developer
Sep 18, 2016 · Artificial Intelligence

How Linear Regression Can Tame Your Nighttime Alert Fatigue

This article explores how historical monitoring alerts can be analyzed and predicted using linear regression, guiding operations engineers to preprocess data, build regression models, and forecast future alert trends to reduce manual alarm handling and improve system stability.

MonitoringOperationsalert prediction
0 likes · 8 min read
How Linear Regression Can Tame Your Nighttime Alert Fatigue
Alibaba Cloud Developer
Alibaba Cloud Developer
Sep 13, 2016 · Artificial Intelligence

How Game Theory and AI Stop Fake Reviews on E‑Commerce Platforms

This article explains how Alibaba combines big‑data analytics, machine learning, and mechanism‑design game theory to create a recommendation system that removes incentives for merchants to generate fake orders, improving fairness and user experience on e‑commerce platforms.

Game TheoryRecommendation Systemsanti-fraud
0 likes · 3 min read
How Game Theory and AI Stop Fake Reviews on E‑Commerce Platforms
Ctrip Technology
Ctrip Technology
Sep 10, 2016 · Artificial Intelligence

Deep Learning Anti‑Scam Guide: An Informal Introduction to Neural Networks, Training, and Practical Applications

This article provides a light‑hearted yet thorough overview of deep learning, covering neural network fundamentals, layer construction, back‑propagation, ResNet shortcuts, encoder‑decoder structures, PU‑learning for unlabeled data, GPU acceleration, and practical advice on data size, frameworks, and deployment in financial scenarios.

BackpropagationBig DataGPU
0 likes · 27 min read
Deep Learning Anti‑Scam Guide: An Informal Introduction to Neural Networks, Training, and Practical Applications
Qunar Tech Salon
Qunar Tech Salon
Aug 21, 2016 · Artificial Intelligence

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

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

Learning-to-Rankfeature engineeringhotel ranking
0 likes · 7 min read
Hotel Search Ranking: Problem Definition, Model Construction, Feature Engineering, and Offline Evaluation
Qunar Tech Salon
Qunar Tech Salon
Aug 20, 2016 · Artificial Intelligence

Personalized Demand Prediction and Ranking for Qunar App’s “You May Like” Card

This article describes how Qunar replaced a low‑click hot‑words card with a personalized “You May Like” recommendation card, detailing data collection, rule‑based and collaborative‑filtering association methods, learning‑to‑rank models (subjective Bayes, RankBoost, LambdaMart), training‑sample strategies, online experiments, evaluation metrics, and future plans including LSTM‑based sequence modeling.

QunarRankingcollaborative filtering
0 likes · 14 min read
Personalized Demand Prediction and Ranking for Qunar App’s “You May Like” Card
Qunar Tech Salon
Qunar Tech Salon
Aug 19, 2016 · Artificial Intelligence

Deep Learning Anti‑Scam Guide: A Non‑Technical Overview of Neural Networks, Training, and Practical Tips

This article provides a humorous yet informative, non‑mathematical guide to deep learning, covering neural network basics, layer addition, training methods, back‑propagation, unsupervised pre‑training, regularization, ResNet shortcuts, GPU computation, framework choices, and practical advice for applying deep learning to industrial data.

AIGPUPu-Learning
0 likes · 26 min read
Deep Learning Anti‑Scam Guide: A Non‑Technical Overview of Neural Networks, Training, and Practical Tips
Qunar Tech Salon
Qunar Tech Salon
Aug 18, 2016 · Artificial Intelligence

Automatic Ticket Classification Using SVM and word2vec at Qunar

At Qunar, the data center algorithm team developed an automatic ticket classification system that combines Support Vector Machine with word2vec embeddings to handle high‑dimensional, low‑sample text data, achieving 89% accuracy and 80% recall while outlining the full machine‑learning pipeline from feature extraction to deployment.

QunarText ClassificationWord2Vec
0 likes · 7 min read
Automatic Ticket Classification Using SVM and word2vec at Qunar
Architecture Digest
Architecture Digest
Aug 15, 2016 · Big Data

Understanding Data: Types, Systems, and Big Data Technologies

This article explains what data is, classifies it into structured, semi‑structured and unstructured forms, describes data mining, databases, data warehouses, the full data lifecycle, and surveys the big‑data ecosystem including storage, batch and real‑time processing, resource scheduling, and visualization technologies.

Data EngineeringLambda architecturedata mining
0 likes · 22 min read
Understanding Data: Types, Systems, and Big Data Technologies
Aotu Lab
Aotu Lab
Aug 10, 2016 · Artificial Intelligence

Can AI Teach Computers to Design Fonts? A Journey into Automated Typography

The article explores the author's experiments combining artificial intelligence with typography, detailing the development of algorithms that measure font attributes, compute similarity scores, and generate rule‑based design systems, while reflecting on the challenges, inspirations, and future possibilities of AI‑driven font selection and design.

AIalgorithmdesign systems
0 likes · 21 min read
Can AI Teach Computers to Design Fonts? A Journey into Automated Typography
Baidu Intelligent Testing
Baidu Intelligent Testing
Jul 13, 2016 · Artificial Intelligence

Detecting Offline Merchant Service Issues Using Machine Learning and Big Data at Nuomi

The article describes how Nuomi analyzes refund and complaint data with machine‑learning and big‑data techniques, extracts features for single‑ and multi‑store scenarios, builds decision‑tree models with regional adjustments, and creates an online workflow to promptly intervene on merchants that fail to serve customers.

Big DataCustomer ExperienceDecision Tree
0 likes · 5 min read
Detecting Offline Merchant Service Issues Using Machine Learning and Big Data at Nuomi
Qunar Tech Salon
Qunar Tech Salon
Jul 4, 2016 · Information Security

Xiaomi Risk Control Practices: Architecture, Rule Engine, and Machine Learning

Xiaomi senior R&D engineer Deng Wenjun shares the evolution of Xiaomi's internet‑finance risk‑control system, describing early rule‑based limits, the adoption of Drools for fast rule deployment, data‑driven modeling with random‑forest classifiers, and ongoing challenges in scalability, latency, and privacy.

DroolsRandom ForestRule Engine
0 likes · 16 min read
Xiaomi Risk Control Practices: Architecture, Rule Engine, and Machine Learning
High Availability Architecture
High Availability Architecture
Jun 24, 2016 · Information Security

Xiaomi's Internet Finance Risk Control Practices: Architecture, Rules Engine, and Machine Learning

The article details Xiaomi's evolution of internet‑finance risk control—from early limit and frequency rules that cut bad‑debt by a third, through adopting the Drools rules engine for rapid deployment and gray‑release, to leveraging random‑forest machine‑learning models and extensive user profiling that reduced fraud by roughly 40%, while addressing privacy and operational challenges.

DroolsRandom ForestXiaomi
0 likes · 15 min read
Xiaomi's Internet Finance Risk Control Practices: Architecture, Rules Engine, and Machine Learning
21CTO
21CTO
Jun 16, 2016 · Big Data

Building a Simple Open‑Source Self‑Service BI Platform with Flask & React

This article introduces dataplay2, an open‑source self‑service BI platform built with Flask, pandas, scikit‑learn on the backend and React, ECharts, D3, and other JavaScript libraries on the frontend, detailing its architecture, installation steps, core features such as data upload, visualization, classification, clustering, and future improvement ideas.

BIdata analysismachine learning
0 likes · 11 min read
Building a Simple Open‑Source Self‑Service BI Platform with Flask & React
21CTO
21CTO
Jun 11, 2016 · Artificial Intelligence

Designing System & Personalized Recommendations Using Mahout

This article outlines the design of both system-wide and personalized recommendation modules for e‑commerce platforms, explains three recommendation approaches (demographic, content‑based, collaborative filtering), details the implementation of Mahout’s collaborative‑filtering algorithm with Java code, discusses data sources, technology stack, algorithm choices, and solutions to the cold‑start problem.

Mahoutcollaborative filteringe-commerce
0 likes · 14 min read
Designing System & Personalized Recommendations Using Mahout
Architecture Digest
Architecture Digest
May 11, 2016 · Artificial Intelligence

Interest Feeds: From Facebook NewsFeed and EdgeRank to Pinterest Smart Feed and General Techniques

This article explains why interest‑driven feeds are essential, reviews Facebook's NewsFeed evolution and EdgeRank algorithm, details Pinterest's Smart Feed architecture and Pinnability model, and provides a comprehensive guide to building, ranking, and monitoring generic interest‑feed systems for social platforms.

FacebookPinterestalgorithm
0 likes · 34 min read
Interest Feeds: From Facebook NewsFeed and EdgeRank to Pinterest Smart Feed and General Techniques
Meituan Technology Team
Meituan Technology Team
Apr 29, 2016 · Big Data

Introduction to Spark in Big Data

Apache Spark, a versatile big‑data platform supporting batch processing, SQL queries, real‑time streaming, and machine‑learning workloads, dramatically accelerates data‑intensive jobs, as demonstrated by Meituan‑Dianping, where its high‑performance engine reduces execution times and enhances scalability across diverse analytical and operational pipelines.

Batch ProcessingBig DataSpark
0 likes · 1 min read
Introduction to Spark in Big Data
Architecture Digest
Architecture Digest
Apr 22, 2016 · Artificial Intelligence

An Introductory Overview of Recommendation Systems and Their Core Algorithms

This article introduces the basic concepts, purposes, and a range of algorithms—including popularity‑based, collaborative filtering, content‑based, model‑based, and hybrid methods—used in recommendation systems, and discusses evaluation metrics and improvement strategies for practical deployment.

AIRecommendation Systemscollaborative filtering
0 likes · 15 min read
An Introductory Overview of Recommendation Systems and Their Core Algorithms
Big Data and Microservices
Big Data and Microservices
Apr 19, 2016 · Industry Insights

Designing a Scalable Real‑Time Stock Prediction Architecture with Open‑Source Tools

This article outlines a reference architecture for a low‑latency, horizontally scalable real‑time stock prediction system built with open‑source components such as Spring Cloud Data Flow, Apache Geode, Spark MLlib, and Hadoop, and discusses data flow steps, simplified deployment, and algorithm choices for market forecasting.

Big DataReal-timemachine learning
0 likes · 7 min read
Designing a Scalable Real‑Time Stock Prediction Architecture with Open‑Source Tools
Java High-Performance Architecture
Java High-Performance Architecture
Apr 18, 2016 · Big Data

Why Spark Is Outpacing Hadoop: Speed, Real‑Time Processing, and ML Advantages

The article explains how Spark has become the leading open‑source big‑data platform, highlighting its superior speed, in‑memory processing, real‑time streaming, and built‑in machine‑learning library compared with Hadoop’s slower, disk‑based MapReduce approach and reliance on external storage and ML tools.

Big DataHadoopReal-time Processing
0 likes · 5 min read
Why Spark Is Outpacing Hadoop: Speed, Real‑Time Processing, and ML Advantages
21CTO
21CTO
Apr 14, 2016 · Big Data

How Meituan’s Data Architecture Powers Precise Mobile Marketing

This article details Meituan Dianping's data‑driven approach to precise marketing, describing the O2O marketing framework, a layered pyramid data system, profiling techniques, budget monitoring, and two real‑world case studies that together illustrate how big‑data technologies boost marketing efficiency on mobile platforms.

Big DataData Architecturemachine learning
0 likes · 12 min read
How Meituan’s Data Architecture Powers Precise Mobile Marketing
Architecture Digest
Architecture Digest
Apr 14, 2016 · Big Data

Data‑Driven Precise Marketing: Architecture and Case Studies from Meituan Dianping

This article presents Meituan Dianping's data‑driven precise marketing architecture, detailing a layered pyramid system, user profiling, budget monitoring, and two real‑world cases—potential user mining and a smart coupon engine—demonstrating how big‑data techniques improve marketing efficiency and ROI.

Data ArchitectureMeituanmachine learning
0 likes · 12 min read
Data‑Driven Precise Marketing: Architecture and Case Studies from Meituan Dianping
Qunar Tech Salon
Qunar Tech Salon
Mar 29, 2016 · Fundamentals

Overview of Ten Classic Algorithms: Sorting, Searching, Graph Traversal, and Machine Learning

This article presents concise explanations and step‑by‑step procedures for ten classic algorithms—including quick sort, heap sort, merge sort, binary search, BFPRT selection, depth‑first and breadth‑first graph traversals, Dijkstra’s shortest‑path method, dynamic programming principles, and the Naive Bayes classifier—highlighting their complexities and core ideas.

Searchingalgorithm fundamentalsdynamic programming
0 likes · 11 min read
Overview of Ten Classic Algorithms: Sorting, Searching, Graph Traversal, and Machine Learning
Architecture Digest
Architecture Digest
Mar 29, 2016 · Artificial Intelligence

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

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

Model Trainingdata preparationfeature engineering
0 likes · 17 min read
Practical Guide to Machine Learning: Problem Modeling, Data Preparation, Feature Engineering, Model Training and Optimization
21CTO
21CTO
Mar 18, 2016 · Artificial Intelligence

10 Essential Tips for Building High‑Performance Intelligent Recommendation Systems

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

Evaluationcold-startdata mining
0 likes · 18 min read
10 Essential Tips for Building High‑Performance Intelligent Recommendation Systems
Meitu Technology
Meitu Technology
Mar 11, 2016 · Artificial Intelligence

Meipai Personalized Recommendation Technology Journey

As Meipai’s user base exploded, the platform shifted from manual curation to sophisticated personalized recommendation algorithms—leveraging machine‑learning and data‑mining techniques, iterating through multiple generations, overcoming scalability and relevance challenges, and delivering rapid solutions that inspire future recommendation system designs.

MeipaiRecommendation Algorithmalgorithm evolution
0 likes · 1 min read
Meipai Personalized Recommendation Technology Journey
Architect
Architect
Mar 10, 2016 · Big Data

Analysis and Practice of a Real-Time Hadoop Data Security Solution

The article presents a detailed technical overview of Apache Eagle's real-time Hadoop data security architecture, covering distributed data collection, stream processing, metadata‑driven policy enforcement, machine‑learning‑based anomaly detection, and integration with Hadoop ecosystem components such as HBase, Kafka, and Storm.

Apache EagleBig DataData Security
0 likes · 25 min read
Analysis and Practice of a Real-Time Hadoop Data Security Solution
ITPUB
ITPUB
Mar 1, 2016 · Artificial Intelligence

10 Essential Machine Learning Algorithms with Python and R Cheat Sheets

This article warns against abandoning machine learning near the finish line and offers a concise cheat‑sheet of the ten most commonly used algorithms, complete with ready‑to‑run Python and R code examples to help practitioners accelerate model development.

AIRmachine learning
0 likes · 3 min read
10 Essential Machine Learning Algorithms with Python and R Cheat Sheets
21CTO
21CTO
Feb 29, 2016 · Fundamentals

Master 10 Essential Algorithms: From QuickSort to Naive Bayes

This article presents concise explanations, step‑by‑step procedures, and visual illustrations for ten core algorithms—including QuickSort, HeapSort, MergeSort, Binary Search, BFPRT, DFS, BFS, Dijkstra, Dynamic Programming, and Naive Bayes—highlighting their principles, complexities, and typical use cases.

Search AlgorithmsSorting Algorithmsdynamic programming
0 likes · 15 min read
Master 10 Essential Algorithms: From QuickSort to Naive Bayes
21CTO
21CTO
Feb 27, 2016 · Artificial Intelligence

How User‑Based Collaborative Filtering Powers Modern Recommendation Systems

This article explains the fundamentals of recommendation algorithms, focusing on user‑based collaborative filtering, similarity metrics, neighbor selection, scoring methods, practical implementation with the MovieLens dataset, and common challenges such as popularity bias and dirty data.

collaborative filteringmachine learningmovie recommendation
0 likes · 12 min read
How User‑Based Collaborative Filtering Powers Modern Recommendation Systems
21CTO
21CTO
Feb 17, 2016 · Big Data

How Big Data Powers Personalized Recommendations in Mother‑Baby E‑Commerce

This article explains the unique characteristics of mother‑baby e‑commerce, describes a comprehensive big‑data platform architecture—including data collection, offline and real‑time computing, and recommendation algorithms—and shows how user profiling and personalized ranking dramatically improve conversion and user experience.

e-commercemachine learningpersonalization
0 likes · 11 min read
How Big Data Powers Personalized Recommendations in Mother‑Baby E‑Commerce
21CTO
21CTO
Feb 12, 2016 · Artificial Intelligence

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

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

Red MansionsSupport Vector MachineText Classification
0 likes · 6 min read
Can Machine Learning Reveal the True Author of Red Mansions' Final 40 Chapters?
Qunar Tech Salon
Qunar Tech Salon
Feb 6, 2016 · Big Data

An Introduction to Data Mining Algorithms and Their Real-World Applications

This article introduces the main types of data‑mining algorithms—classification, prediction, clustering, and association—explains supervised and unsupervised learning, and illustrates each with practical examples such as spam detection, tumor cell identification, wine quality assessment, fraud detection, recommendation systems, and more.

Clusteringassociation analysisclassification
0 likes · 15 min read
An Introduction to Data Mining Algorithms and Their Real-World Applications
Qunar Tech Salon
Qunar Tech Salon
Jan 13, 2016 · Artificial Intelligence

Ranking Learning in Mobile Taobao: Challenges, Solutions, and Improvements

This article presents a comprehensive overview of ranking learning techniques used in Mobile Taobao's recommendation system, covering problem definition, pointwise/pairwise/listwise approaches, feature engineering, online learning, industry applications, and future optimization strategies.

CTR PredictionLambdaMARTRanking
0 likes · 8 min read
Ranking Learning in Mobile Taobao: Challenges, Solutions, and Improvements
21CTO
21CTO
Jan 11, 2016 · Artificial Intelligence

How WeChat Serves Tailored Ads: Inside the Recommendation Algorithm

This article explains the content‑based recommendation technique behind WeChat Moments ads, illustrates how user behavior is matched to ad attributes, and offers practical tips for influencing the system to display high‑value ads such as BMW.

WeChat advertisingcontent-based filteringmachine learning
0 likes · 5 min read
How WeChat Serves Tailored Ads: Inside the Recommendation Algorithm
Huawei Cloud Developer Alliance
Huawei Cloud Developer Alliance
Jan 8, 2016 · Artificial Intelligence

Can Open‑Source AI Predict the Stock Market? Inside a Real‑Time Forecasting Architecture

The article examines the suspension of China's stock‑market circuit‑breaker, then explores whether open‑source frameworks and machine‑learning algorithms can realistically forecast stock prices by leveraging massive historical data, real‑time streams, and sentiment analysis from social media and news sources.

financial time seriesmachine learningreal-time architecture
0 likes · 9 min read
Can Open‑Source AI Predict the Stock Market? Inside a Real‑Time Forecasting Architecture
21CTO
21CTO
Jan 6, 2016 · Artificial Intelligence

From Naïve Algorithms to Scalable Recommendations: Jiayuan’s Journey

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

Recommendation Systemsfeature engineeringlogistic regression
0 likes · 15 min read
From Naïve Algorithms to Scalable Recommendations: Jiayuan’s Journey
Efficient Ops
Efficient Ops
Jan 5, 2016 · Information Security

How Apache Eagle Secures Hadoop: Real‑Time Big Data Threat Detection

Apache Eagle is an open‑source, distributed, real‑time security monitoring platform for Hadoop that combines stream‑processing, scalable policy enforcement, and machine‑learning user profiling to protect massive data assets across eBay’s production clusters.

Apache EagleBig DataHadoop
0 likes · 19 min read
How Apache Eagle Secures Hadoop: Real‑Time Big Data Threat Detection
21CTO
21CTO
Jan 3, 2016 · Artificial Intelligence

How to Build a Real-Time Stock Prediction System with Open-Source AI and Big Data Tools

An open-source reference architecture for real-time stock prediction is presented, detailing a scalable, low-latency pipeline that captures live market data, stores it in memory, trains and applies machine learning models using Spring Cloud Data Flow, Apache Geode, Spark MLlib, and related big‑data components.

Big DataSpark MLlibSpring Cloud Data Flow
0 likes · 8 min read
How to Build a Real-Time Stock Prediction System with Open-Source AI and Big Data Tools
Architect
Architect
Dec 31, 2015 · Big Data

Using Spark for Machine Learning, New Word Discovery, and Intelligent Q&A

The article explains how to leverage Apache Spark for machine‑learning tasks, large‑scale new‑word discovery, and simple intelligent question‑answering by using Spark‑Shell, Scala code, and word2vec‑based similarity, while sharing practical tips and performance considerations.

Big DataIntelligent QANew Word Discovery
0 likes · 15 min read
Using Spark for Machine Learning, New Word Discovery, and Intelligent Q&A
Qunar Tech Salon
Qunar Tech Salon
Dec 29, 2015 · Artificial Intelligence

Technical Debt in Machine Learning Systems

The paper examines how machine‑learning systems inherit unique forms of technical debt—such as boundary erosion, entanglement, hidden feedback loops, and data‑dependency issues—and discusses mitigation strategies, measurement techniques, and cultural changes needed to maintain sustainable, reliable ML deployments.

data dependenciesfeedback loopsmachine learning
0 likes · 26 min read
Technical Debt in Machine Learning Systems
Efficient Ops
Efficient Ops
Dec 5, 2015 · Information Security

Cultivating Secure Development Talent, Effective Security Visualization, and the Role of Machine Learning

This article shares insights from a security‑focused discussion on nurturing security‑oriented developers, balancing leadership and analyst needs in security visualization, and evaluating whether machine‑learning techniques truly add value to internal security data processing.

DevSecOpsinformation securitymachine learning
0 likes · 7 min read
Cultivating Secure Development Talent, Effective Security Visualization, and the Role of Machine Learning
Architects Research Society
Architects Research Society
Dec 3, 2015 · Artificial Intelligence

IBM Donates SystemML to Apache Incubator, Joining the Open‑Source Machine Learning Wave

IBM announced that its SystemML machine‑learning platform will become an Apache Incubator project, highlighting a broader industry trend where tech giants like Google and Facebook open‑source their AI tools to accelerate data‑driven innovation and expand enterprise‑focused machine‑learning ecosystems.

Apache SystemMLBig DataIBM
0 likes · 5 min read
IBM Donates SystemML to Apache Incubator, Joining the Open‑Source Machine Learning Wave
21CTO
21CTO
Nov 20, 2015 · Artificial Intelligence

How Meituan Builds and Optimizes Its Recommendation System

This article explains Meituan's end‑to‑end recommendation system architecture, data processing pipeline, candidate generation strategies, model training and online ranking techniques, illustrating how data, algorithms, and real‑time signals are combined to improve relevance and conversion.

AIData EngineeringMeituan
0 likes · 19 min read
How Meituan Builds and Optimizes Its Recommendation System
ITPUB
ITPUB
Nov 13, 2015 · Fundamentals

What Defines Data Science? Core Steps and Essential Book Recommendations

The article outlines data science as an interdisciplinary field centered on three key steps—pre‑processing, interpretation, and modeling—while providing concise recommendations of foundational books for R, Python, exploratory analysis, machine learning, and essential tools to guide practitioners.

Book RecommendationsR programmingdata analysis
0 likes · 16 min read
What Defines Data Science? Core Steps and Essential Book Recommendations
Art of Distributed System Architecture Design
Art of Distributed System Architecture Design
Oct 29, 2015 · Big Data

TalkingData’s Journey to Building a Mobile Big Data Platform with Spark and YARN

This article recounts how TalkingData progressively introduced Spark into its Hadoop‑YARN based mobile big‑data platform, detailing early architectures, migration challenges, performance gains, the fully Spark‑centric redesign with Kafka and Spark Streaming, encountered pitfalls, and future plans for further optimization.

Data PlatformHadoopSpark
0 likes · 16 min read
TalkingData’s Journey to Building a Mobile Big Data Platform with Spark and YARN
21CTO
21CTO
Oct 26, 2015 · Artificial Intelligence

How Weibo’s Recommendation Engine Evolved: From 1.0 to Platform‑Scale 3.0

This article traces the evolution of Weibo's recommendation architecture across three major phases—independent 1.0, layered 2.0, and platform‑centric 3.0—detailing the driving business and technical factors, architectural components, advantages, shortcomings, and key outcomes of each stage.

AI EngineeringWeiboarchitecture evolution
0 likes · 19 min read
How Weibo’s Recommendation Engine Evolved: From 1.0 to Platform‑Scale 3.0
21CTO
21CTO
Oct 24, 2015 · Artificial Intelligence

Building an Offline Recommendation System with Mahout: Practical Steps and Tips

This article walks through the end‑to‑end process of building an offline recommendation system using Mahout, covering data collection, filtering, storage, various collaborative‑filtering algorithms, similarity measures, evaluation metrics, parameter tuning, AB testing, and spam‑fighting strategies.

Mahoutcollaborative filteringmachine learning
0 likes · 16 min read
Building an Offline Recommendation System with Mahout: Practical Steps and Tips
Qunar Tech Salon
Qunar Tech Salon
Oct 22, 2015 · Artificial Intelligence

Airbnb’s Dynamic Pricing System and Machine‑Learning Platform (Aerosolve)

The article describes how Airbnb built and continuously improved a machine‑learning‑driven dynamic pricing tool—Aerosolve—that extracts property features, compares similar listings, incorporates seasonal and event‑driven demand, and automatically updates nightly price suggestions to help hosts set optimal rates.

AirbnbPrice Optimizationalgorithm
0 likes · 18 min read
Airbnb’s Dynamic Pricing System and Machine‑Learning Platform (Aerosolve)
21CTO
21CTO
Oct 16, 2015 · Artificial Intelligence

Mastering Industrial Machine Learning: From Problem Modeling to Model Optimization

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

Industrial ApplicationModel Trainingdata preparation
0 likes · 20 min read
Mastering Industrial Machine Learning: From Problem Modeling to Model Optimization