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1959 articles · Page 14 of 20
Taobao Frontend Technology
Taobao Frontend Technology
Jun 2, 2020 · Artificial Intelligence

How AI Can Auto‑Detect UI Components for Seamless Front‑End Code Generation

This article explains how to use deep‑learning object detection to automatically recognize UI components in design drafts, generate a smart JSON description, and convert it into component‑based front‑end code, covering problem analysis, dataset preparation, algorithm selection, model training, evaluation, and deployment.

AIMachine LearningPipcook
0 likes · 30 min read
How AI Can Auto‑Detect UI Components for Seamless Front‑End Code Generation
Architect
Architect
May 29, 2020 · Artificial Intelligence

Integrating Flink with TensorFlow for End-to-End Machine Learning Pipelines

This article explains how to combine the Flink data‑processing engine with TensorFlow to create a unified, end‑to‑end machine‑learning workflow, covering background, challenges, the Flink‑AI‑extended architecture, ML framework and operator abstractions, and both batch and streaming training and prediction modes.

AI integrationData ProcessingDistributed Training
0 likes · 9 min read
Integrating Flink with TensorFlow for End-to-End Machine Learning Pipelines
DataFunTalk
DataFunTalk
May 29, 2020 · Artificial Intelligence

Model‑Independent Learning: Multi‑Task Learning and Transfer Learning

This article explains two model‑independent learning paradigms—multi‑task learning and transfer learning—detailing their motivations, sharing mechanisms, training procedures, theoretical formulations, and practical benefits such as improved generalization, data efficiency, and domain‑invariant representations.

Deep LearningDomain AdaptationMachine Learning
0 likes · 21 min read
Model‑Independent Learning: Multi‑Task Learning and Transfer Learning
JD Tech Talk
JD Tech Talk
May 29, 2020 · Artificial Intelligence

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

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

Machine LearningModel Stabilitydata preprocessing
0 likes · 14 min read
The Black Art of Feature Engineering: Importance, Techniques, and Automation
Sohu Tech Products
Sohu Tech Products
May 27, 2020 · Artificial Intelligence

Overview of Graph Embedding Techniques: DeepWalk, LINE, node2vec, and EGES

This article provides a comprehensive overview of graph embedding methods—including DeepWalk, LINE, node2vec, and EGES—explaining their algorithms, random‑walk strategies, proximity definitions, incorporation of side information, and their applications in large‑scale recommendation systems.

DeepWalkMachine LearningRecommendation Systems
0 likes · 20 min read
Overview of Graph Embedding Techniques: DeepWalk, LINE, node2vec, and EGES
DataFunTalk
DataFunTalk
May 26, 2020 · Artificial Intelligence

Knowledge Distillation Techniques for Recommendation Systems: Methods, Scenarios, and Practical Insights

This article reviews how knowledge distillation—using a large teacher model to guide a smaller student model—can be applied across the recall, coarse‑ranking, and fine‑ranking stages of recommendation systems, detailing logits‑based and feature‑based approaches, joint and two‑stage training, and point‑wise, pair‑wise, and list‑wise loss designs.

Machine LearningRecommendation Systemsknowledge distillation
0 likes · 31 min read
Knowledge Distillation Techniques for Recommendation Systems: Methods, Scenarios, and Practical Insights
Huawei Cloud Developer Alliance
Huawei Cloud Developer Alliance
May 25, 2020 · Artificial Intelligence

How AI Turns Dark Data into Actionable Automation

This article explains how enterprises can classify structured, semi‑structured and unstructured data as “dark data”, why traditional RPA struggles with it, and how AI technologies like NLP, computer vision and machine learning—exemplified by Automation Anywhere’s IQ‑Bot—enable end‑to‑end automation of hidden information.

AIIQ BotMachine Learning
0 likes · 9 min read
How AI Turns Dark Data into Actionable Automation
Taobao Frontend Technology
Taobao Frontend Technology
May 25, 2020 · Frontend Development

How to Build Front‑End AI Experiments with Pipcook: From Setup to Real‑World Image Classification

This comprehensive guide walks front‑end developers through preparing hardware and OS, installing Python and Node environments, launching Pipcook's visual board, running handwritten digit and image classification experiments, creating and augmenting training samples, configuring pipelines, training models, and understanding deployment, all using the Pipcook framework.

Data AugmentationMachine Learningimage classification
0 likes · 34 min read
How to Build Front‑End AI Experiments with Pipcook: From Setup to Real‑World Image Classification
FunTester
FunTester
May 25, 2020 · Artificial Intelligence

How AI Can Eliminate Selenium’s ‘Element Not Found’ Nightmares

When web applications become highly dynamic, traditional Selenium locators become fragile and costly to maintain, but leveraging AI and machine‑learning‑based object‑recognition frameworks can dramatically reduce maintenance overhead and accelerate test creation by intelligently adapting to UI changes.

AIDynamic UIMachine Learning
0 likes · 2 min read
How AI Can Eliminate Selenium’s ‘Element Not Found’ Nightmares
DataFunTalk
DataFunTalk
May 24, 2020 · Artificial Intelligence

Automatic Calibration of Road Intersection Topology Using Trajectories (CITT Framework)

This article presents the CITT framework, a three‑stage algorithm that automatically calibrates road‑intersection topology using massive GPS trajectory data, detailing preprocessing, core‑area detection via quadtree‑mean‑shift, influence‑zone calibration with direction‑weighted Frechet distance and DBSCAN, and demonstrating superior accuracy over existing methods.

Machine Learningintersection calibrationmap updating
0 likes · 10 min read
Automatic Calibration of Road Intersection Topology Using Trajectories (CITT Framework)
DataFunTalk
DataFunTalk
May 21, 2020 · Artificial Intelligence

Query Expansion Techniques for Search Optimization: Models, Data Sources, and Practical Practices

This article reviews the factors influencing search results, explains why query expansion is crucial for improving recall, surveys various sources of expansion terms, describes probabilistic and translation‑based models, and offers practical recommendations for building effective, data‑driven query expansion pipelines.

Knowledge GraphMachine LearningQuery Expansion
0 likes · 11 min read
Query Expansion Techniques for Search Optimization: Models, Data Sources, and Practical Practices
Alibaba Terminal Technology
Alibaba Terminal Technology
May 20, 2020 · Frontend Development

Turn Your Front‑End into an AI Playground: Hands‑On Pipcook Tutorial

This comprehensive guide walks you through setting up a front‑end intelligent environment with Pipcook, covering hardware choices, OS configuration, Python and Node setups, quick visual experiments, data organization, sample generation, augmentation, feature engineering, model training, and principle analysis for digit and image classification tasks.

Machine Learningfrontendnodejs
0 likes · 29 min read
Turn Your Front‑End into an AI Playground: Hands‑On Pipcook Tutorial
DataFunTalk
DataFunTalk
May 13, 2020 · Artificial Intelligence

Designing and Scaling Recommendation Systems for Cross‑border E‑commerce Growth

This article shares the author’s experience at Club Factory, describing the business model, growth challenges, macro‑ and micro‑level analysis, and detailed technical breakdowns of recommendation system components—including recall, ranking, user interest modeling, evaluation metrics, and ecosystem considerations—to guide scalable e‑commerce growth.

E‑commerceGrowth StrategyMachine Learning
0 likes · 17 min read
Designing and Scaling Recommendation Systems for Cross‑border E‑commerce Growth
Tencent Music Tech Team
Tencent Music Tech Team
May 8, 2020 · Mobile Development

Mobile Machine Learning Frameworks Overview and Deployment Practices in Q Music

The article reviews four mobile‑focused machine‑learning frameworks—NCNN, TensorFlow Lite, PyTorch Mobile (Caffe2) and FeatherKit—detailing their size, speed, and resource trade‑offs, and explains Q Music’s edge‑inference pipeline, optimization strategies, and the challenges of performance variability on heterogeneous mobile devices.

FeatherKitFramework ComparisonMachine Learning
0 likes · 25 min read
Mobile Machine Learning Frameworks Overview and Deployment Practices in Q Music
DataFunTalk
DataFunTalk
May 7, 2020 · Artificial Intelligence

Comprehensive Overview of Query Understanding in Search Engines

Query understanding (QU) involves lexical, syntactic, and semantic analysis of user queries to enable effective search recall and ranking, covering modules such as preprocessing, correction, expansion, segmentation, intent detection, term importance, and guidance, with detailed discussion of algorithms, models, and system architecture.

Machine LearningNLPinformation retrieval
0 likes · 51 min read
Comprehensive Overview of Query Understanding in Search Engines
Python Programming Learning Circle
Python Programming Learning Circle
May 7, 2020 · Artificial Intelligence

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

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

KNNMachine Learningclassification
0 likes · 10 min read
Understanding the k-Nearest Neighbor (kNN) Classification Algorithm and Its Python Implementation
HomeTech
HomeTech
May 7, 2020 · Big Data

Construction and Evaluation of User Profiles: Identification, Tagging, Storage, and Quality Assessment

This article explains how to build user profiles by distinguishing persona from profile, describing the evolution of ID‑mapping techniques, designing a multi‑layer tag system, implementing statistical, interest, and model tags, storing the data in Hive, HBase, Codis and Elasticsearch, and finally evaluating profile timeliness, coverage and accuracy.

Big DataData StorageMachine Learning
0 likes · 11 min read
Construction and Evaluation of User Profiles: Identification, Tagging, Storage, and Quality Assessment
Tencent Advertising Technology
Tencent Advertising Technology
May 5, 2020 · Artificial Intelligence

How to Use the TI-ONE SDK to Train Models for the 2020 Tencent Advertising Algorithm Competition

This tutorial walks you through the complete process of using the TI-ONE SDK—including data preparation, dependency installation, session initialization, TensorFlow estimator configuration, job submission, and result monitoring—to train a machine‑learning model for the 2020 Tencent Advertising Algorithm Competition.

Machine LearningSDKTI-ONE
0 likes · 7 min read
How to Use the TI-ONE SDK to Train Models for the 2020 Tencent Advertising Algorithm Competition
Tencent Advertising Technology
Tencent Advertising Technology
May 2, 2020 · Artificial Intelligence

How to Use TI-ONE Built‑in Operators for the 2020 Tencent Advertising Algorithm Competition

This tutorial walks you through creating a TI‑ONE project, ingesting competition data, configuring and training a decision‑tree model with built‑in operators, running the workflow, and downloading and uploading the result files for the 2020 Tencent Advertising Algorithm Competition.

Data PipelineMachine LearningTI-ONE
0 likes · 7 min read
How to Use TI-ONE Built‑in Operators for the 2020 Tencent Advertising Algorithm Competition
DataFunTalk
DataFunTalk
Apr 17, 2020 · Artificial Intelligence

Data Privacy and Differential Privacy Techniques for Machine Learning

The article reviews the growing importance of data privacy in machine learning, explains privacy concepts and attack vectors, and details anonymization methods such as k‑anonymity, l‑diversity, t‑closeness, as well as differential privacy techniques and their practical applications.

Differential PrivacyMachine Learningdata privacy
0 likes · 13 min read
Data Privacy and Differential Privacy Techniques for Machine Learning
DataFunTalk
DataFunTalk
Apr 13, 2020 · Artificial Intelligence

Deep Spatio‑Temporal Neural Networks and Memory‑Augmented DNN for Click‑Through Rate Prediction

This article presents the design, challenges, and experimental evaluation of DSTN (with pooling, self‑attention, and interactive‑attention variants) and MA‑DNN models for CTR prediction, highlighting how temporal and contextual ad information improves accuracy and yields significant online gains in large‑scale advertising systems.

AdvertisingCTR predictionDeep Learning
0 likes · 16 min read
Deep Spatio‑Temporal Neural Networks and Memory‑Augmented DNN for Click‑Through Rate Prediction
JD Tech Talk
JD Tech Talk
Apr 8, 2020 · Artificial Intelligence

Designing and Evaluating Recommendation Algorithms for Wealth Management Platforms

This article explores how large wealth‑management platforms can model product recommendation as a mapping between customers and financial products, defines various evaluation goals such as transaction volume, revenue and user satisfaction, and outlines a systematic A/B‑testing workflow for comparing and optimizing recommendation algorithms.

A/B testingAlgorithmMachine Learning
0 likes · 10 min read
Designing and Evaluating Recommendation Algorithms for Wealth Management Platforms
DataFunTalk
DataFunTalk
Apr 5, 2020 · Artificial Intelligence

WeChat Hotspot Mining Platform: Architecture, Detection, and Presentation

This article describes a WeChat hotspot mining platform that integrates multiple data sources, builds quality and prediction models, employs advanced clustering and multi‑granular text matching techniques, and uses generative active learning to efficiently discover, predict, and present news hotspots for users.

Active LearningMachine LearningText classification
0 likes · 17 min read
WeChat Hotspot Mining Platform: Architecture, Detection, and Presentation
iQIYI Technical Product Team
iQIYI Technical Product Team
Apr 3, 2020 · Artificial Intelligence

iCartoonFace Challenge: Cartoon Face Detection and Recognition Competition

The iCartoonFace Challenge invites participants to develop efficient algorithms for detecting and recognizing cartoon faces using large, meticulously annotated datasets—50,000 images for detection and nearly 390,000 for recognition—while meeting strict model size and latency limits and submitting detailed methods and code.

AI CompetitionCartoon Face RecognitionData Set
0 likes · 6 min read
iCartoonFace Challenge: Cartoon Face Detection and Recognition Competition
Youku Technology
Youku Technology
Apr 2, 2020 · Artificial Intelligence

In‑Depth Overview of Intelligent Marketing Uplift Modeling

The talk explains uplift modeling for intelligent marketing, showing how causal lift predictions—derived from randomized experiments using two‑model, one‑model, or tree‑based methods—identify truly responsive users, evaluate performance with AUUC/Qini, and were applied to Taopiaopiao’s coupon allocation via knapsack optimization, highlighting challenges and future directions.

A/B testingMachine LearningUplift Modeling
0 likes · 16 min read
In‑Depth Overview of Intelligent Marketing Uplift Modeling
DataFunTalk
DataFunTalk
Mar 31, 2020 · Artificial Intelligence

Design and Evolution of the Quality Control Framework for WeChat Look Feature

This article presents the overall design, multi‑dimensional control mechanisms, auxiliary modules, and evolutionary processes of the quality control system used in WeChat's Look feature, detailing data lifecycle, model training, generalization, transfer learning, and continuous anti‑abuse strategies.

Machine Learningcontent moderationmodel training
0 likes · 18 min read
Design and Evolution of the Quality Control Framework for WeChat Look Feature
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 30, 2020 · Artificial Intelligence

How AI Is Transforming Language, Speech, and Vision: Key Technologies and Future Trends

This article provides a comprehensive overview of AI's rapid evolution, covering deep learning foundations, machine learning components, natural language processing advances, speech recognition breakthroughs, multimodal interaction, computer vision progress, model compression techniques, and the shift from data‑driven to knowledge‑based AI approaches.

Machine Learningspeech recognition
0 likes · 19 min read
How AI Is Transforming Language, Speech, and Vision: Key Technologies and Future Trends
Python Programming Learning Circle
Python Programming Learning Circle
Mar 26, 2020 · Artificial Intelligence

Understanding Gradient Descent for Linear Regression with a Python Implementation

This article explains the concept of loss functions and gradient descent, illustrates how to find the global optimum for linear regression, discusses the role of learning rate, and provides a complete Python example that generates data, applies gradient descent, and visualizes the results.

Linear RegressionMachine LearningOptimization
0 likes · 6 min read
Understanding Gradient Descent for Linear Regression with a Python Implementation
DataFunTalk
DataFunTalk
Mar 19, 2020 · Artificial Intelligence

Advances in Voice Interaction: 360's Intelligent Dialogue System Architecture and Core Technologies

This article presents a comprehensive overview of 360's voice interaction platform, detailing dialogue system fundamentals, platform architecture, and core technologies such as semantic understanding, dialog management, and question answering, all driven by deep learning and multimodal innovations.

AIKnowledge GraphMachine Learning
0 likes · 16 min read
Advances in Voice Interaction: 360's Intelligent Dialogue System Architecture and Core Technologies
HomeTech
HomeTech
Mar 18, 2020 · Artificial Intelligence

Automobile Home Recommendation System Architecture and Ranking Models

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

AB testingAutoMLMachine Learning
0 likes · 18 min read
Automobile Home Recommendation System Architecture and Ranking Models
DataFunTalk
DataFunTalk
Mar 17, 2020 · Artificial Intelligence

A Survey of Text Data Augmentation Techniques in Natural Language Processing

This article systematically reviews recent developments in text data augmentation for natural language processing, covering common scenarios such as low‑resource and imbalanced classification, and detailing five major techniques—including back‑translation, EDA, TF‑IDF‑based replacement, contextual augmentation, and language‑model‑based methods—with experimental results and future directions.

Data AugmentationMachine LearningNLP
0 likes · 27 min read
A Survey of Text Data Augmentation Techniques in Natural Language Processing
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 17, 2020 · Artificial Intelligence

How AI Engineering Powers Modern Enterprises: From Deep Learning to Cloud Infrastructure

This article explores the fundamentals and evolution of artificial intelligence, its applications in perception and decision‑making, the role of deep learning, the importance of compute power and cloud platforms, and how enterprises can strategically adopt AI and data‑driven solutions to drive business value.

AI infrastructureMachine Learning
0 likes · 15 min read
How AI Engineering Powers Modern Enterprises: From Deep Learning to Cloud Infrastructure
21CTO
21CTO
Mar 16, 2020 · Artificial Intelligence

Why Tesla’s AI Team Needs Both Python and C++: Balancing Speed and Simplicity

The article examines why Tesla’s AI team combines Python for rapid prototyping with C++ for high‑performance inference, discussing the trade‑offs between ease of use and latency, the role of frameworks like TensorFlow and PyTorch, and the broader debate over language choice in modern AI development.

Artificial IntelligenceC++Frameworks
0 likes · 8 min read
Why Tesla’s AI Team Needs Both Python and C++: Balancing Speed and Simplicity
JD Tech Talk
JD Tech Talk
Mar 16, 2020 · Artificial Intelligence

JD Digits' Self‑Developed Intelligent Anti‑Fraud Platform and AI‑Powered Account Security Guarantee

JD Digits explains how its AI‑driven anti‑fraud platform, featuring automatic adversarial machine learning and graph neural networks, underpins a new one‑million‑yuan account security guarantee that proactively protects users from invisible financial fraud while improving the overall user experience.

AIGraph Neural NetworkMachine Learning
0 likes · 10 min read
JD Digits' Self‑Developed Intelligent Anti‑Fraud Platform and AI‑Powered Account Security Guarantee
Xueersi Online School Tech Team
Xueersi Online School Tech Team
Mar 13, 2020 · Artificial Intelligence

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

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

Education AnalyticsLogistic RegressionMachine Learning
0 likes · 9 min read
Predictive Modeling of Student Renewal and Refund Intentions Using Logistic Regression in Online Education
Python Programming Learning Circle
Python Programming Learning Circle
Mar 12, 2020 · Fundamentals

Fundamentals of Derivatives and Partial Derivatives for Neural Networks

This article introduces the mathematical foundations of derivatives and partial derivatives, explains their role in optimizing neural network parameters, covers basic derivative formulas, linear properties, sigmoid derivative, minimum conditions, and constrained optimization using Lagrange multipliers, providing a comprehensive guide for machine‑learning practitioners.

DerivativesMachine LearningOptimization
0 likes · 8 min read
Fundamentals of Derivatives and Partial Derivatives for Neural Networks
ITPUB
ITPUB
Mar 11, 2020 · Artificial Intelligence

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

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

A/B testingMachine LearningReal-time Training
0 likes · 17 min read
Inside Toutiao’s Recommendation Engine: Architecture, Features, and Evaluation
Liangxu Linux
Liangxu Linux
Mar 9, 2020 · Artificial Intelligence

Inside ByteDance’s Recommendation Engine: How TikTok Delivers Billions of Personalized Feeds

ByteDance’s recommendation system models user satisfaction as a function of content, user, and context features, employing diverse algorithms—from logistic regression to deep learning—while leveraging real‑time training, hierarchical text classification, dynamic user tagging, rigorous A/B testing, and multi‑layer content safety checks to deliver personalized feeds at massive scale.

Machine LearningReal-time TrainingRecommendation Systems
0 likes · 19 min read
Inside ByteDance’s Recommendation Engine: How TikTok Delivers Billions of Personalized Feeds
Python Programming Learning Circle
Python Programming Learning Circle
Mar 7, 2020 · Artificial Intelligence

k-Nearest Neighbors (kNN) Algorithm: Overview, Pros/Cons, Data Preparation, Implementation, and Handwritten Digit Recognition

This article explains the k‑Nearest Neighbors classification method, discusses its advantages and drawbacks, describes data preparation and normalization, presents Python code for the algorithm and a full handwritten digit recognition project, and reports an error rate of about 1.2%.

KNNMachine Learningclassification
0 likes · 9 min read
k-Nearest Neighbors (kNN) Algorithm: Overview, Pros/Cons, Data Preparation, Implementation, and Handwritten Digit Recognition
Python Programming Learning Circle
Python Programming Learning Circle
Mar 6, 2020 · Artificial Intelligence

Introduction to Machine Learning Concepts: Data, Features, Labels, Training, and Common Algorithms

This article provides a beginner-friendly overview of machine learning fundamentals, covering the definition of data, the distinction between features and labels, types of features, dimensionality, training and test datasets, normalization, supervised and unsupervised learning methods, algorithm selection, development workflow, and recommended Python libraries such as NumPy.

Machine LearningSupervised LearningUnsupervised Learning
0 likes · 12 min read
Introduction to Machine Learning Concepts: Data, Features, Labels, Training, and Common Algorithms
Python Programming Learning Circle
Python Programming Learning Circle
Mar 5, 2020 · Artificial Intelligence

Biological Neurons and Their Simple Mathematical Representation in Neural Networks

This article explains how biological neurons inspire artificial neural networks, describing neuron concepts, threshold firing, weighted inputs, bias, activation functions such as the step and sigmoid functions, and shows how these ideas are expressed mathematically and visualized with diagrams.

Artificial IntelligenceBiasMachine Learning
0 likes · 13 min read
Biological Neurons and Their Simple Mathematical Representation in Neural Networks
Qudian (formerly Qufenqi) Technology Team
Qudian (formerly Qufenqi) Technology Team
Mar 4, 2020 · Artificial Intelligence

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

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

AIMachine Learningdata engineering
0 likes · 8 min read
How Intelligent Marketing Leverages AI and Big Data to Boost Conversion Rates
DataFunTalk
DataFunTalk
Mar 3, 2020 · Artificial Intelligence

Causal Inference Guided Stable Learning: Improving Explainability and Prediction Stability in Machine Learning

Machine learning models often suffer from poor explainability and unstable predictions due to reliance on spurious correlations, but by applying causal inference to separate true causal relationships from confounding and selection bias, a causal‑constrained stable learning framework can achieve more interpretable and robust predictions across varying data distributions.

ExplainabilityMachine Learningcausal inference
0 likes · 14 min read
Causal Inference Guided Stable Learning: Improving Explainability and Prediction Stability in Machine Learning
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 3, 2020 · Artificial Intelligence

How Alibaba Turns AI, Deep Learning, and Big Data into Enterprise Power

Jia Yangqing’s talk from the Alibaba CIO Academy explains what artificial intelligence is, its applications, the challenges of perception and decision making, the evolution of deep‑learning models, the need for massive compute power, and how enterprises can strategically adopt AI and big‑data technologies to drive innovation.

AI PlatformsCloud ComputingMachine Learning
0 likes · 16 min read
How Alibaba Turns AI, Deep Learning, and Big Data into Enterprise Power
Architecture Digest
Architecture Digest
Mar 2, 2020 · Artificial Intelligence

Recommendation System Architecture and Practices at Toutiao

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

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

Evolution of Autohome's Recommendation System Ranking Algorithms

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

AB testingAIMachine Learning
0 likes · 20 min read
Evolution of Autohome's Recommendation System Ranking Algorithms
Xianyu Technology
Xianyu Technology
Feb 27, 2020 · Artificial Intelligence

Data-Driven Simulation for User Activity Retention Prediction

By extracting hour‑level activity logs and training supervised models—including CART, GBDT, and neural networks—on user tags, the team simulated short‑term metrics for new reward campaigns, enabling earlier prediction of next‑day retention and shortening experiment cycles despite delayed T+1 data.

AB testingCARTGBDT
0 likes · 9 min read
Data-Driven Simulation for User Activity Retention Prediction
DataFunTalk
DataFunTalk
Feb 26, 2020 · Artificial Intelligence

User Growth, Full‑Stack Growth System, and Deep Learning Applications in Search at Alibaba 1688

This article presents a comprehensive overview of Alibaba 1688’s user‑growth strategy, full‑link growth system, intelligent coupon and push mechanisms, and the application of deep‑learning and optimization techniques in search and order‑aggregation, illustrating how data‑driven algorithms drive e‑commerce performance.

AIAlibabaE‑commerce
0 likes · 20 min read
User Growth, Full‑Stack Growth System, and Deep Learning Applications in Search at Alibaba 1688
DataFunTalk
DataFunTalk
Feb 24, 2020 · Artificial Intelligence

Adversarial Training for Transformer‑Based Natural Language Models: Methods, Variants, and Experimental Results

This presentation reviews adversarial training techniques for transformer‑based NLP models, covering the motivation, image‑based and text‑based attack generation, standard PGD, its variants FreeAT and YOPO, the proposed FreeLB method, extensive GLUE experiments, and conclusions about robustness and future directions.

FreeLBMachine LearningNLP
0 likes · 18 min read
Adversarial Training for Transformer‑Based Natural Language Models: Methods, Variants, and Experimental Results
Meituan Technology Team
Meituan Technology Team
Feb 20, 2020 · Operations

Intelligent Delivery System Architecture and Optimization at Meituan

Meituan’s intelligent delivery system integrates operations‑research, machine learning, and IoT across three layers—structural optimization, market adjustment, and real‑time matching—to plan smart areas, schedule riders, route orders, and dispatch efficiently, achieving measurable travel‑distance reductions and significant time savings.

AIMachine LearningRouting
0 likes · 20 min read
Intelligent Delivery System Architecture and Optimization at Meituan
HomeTech
HomeTech
Feb 19, 2020 · Artificial Intelligence

Voiceprint-Based Gender Recognition Using GMM‑UBM and i‑Vector Modeling for 400‑Call Center Audio

This article presents a complete voiceprint gender identification pipeline for 400‑call center recordings, detailing acoustic feature extraction, GMM‑UBM training, Joint Factor Analysis, i‑vector extraction, and logistic regression classification, achieving a reported accuracy of 97.8%.

GMM-UBMMachine Learningacoustic features
0 likes · 11 min read
Voiceprint-Based Gender Recognition Using GMM‑UBM and i‑Vector Modeling for 400‑Call Center Audio
Efficient Ops
Efficient Ops
Feb 18, 2020 · Operations

How Intelligent Ops Transforms Monitoring: Multi‑Dimensional Anomaly Detection & Smart Alert Merging

This article presents the 2019 GOPS Global Operations Conference talk by Gong Cheng, detailing how intelligent monitoring leverages multi‑dimensional anomaly detection, machine‑learning‑based alert merging, knowledge‑graph construction, and root‑cause analysis to automate and improve large‑scale IT operations.

Knowledge GraphMachine LearningRoot Cause Analysis
0 likes · 22 min read
How Intelligent Ops Transforms Monitoring: Multi‑Dimensional Anomaly Detection & Smart Alert Merging
360 Quality & Efficiency
360 Quality & Efficiency
Feb 14, 2020 · Artificial Intelligence

Applying Reinforcement Learning to UI Traversal for Automated Testing

The article explores how reinforcement learning can be used to create a test robot that performs UI traversal, discussing the challenges of full automation, defining the MDP components, feature extraction methods, reward design, and suitable RL algorithms to improve testing coverage and efficiency.

MDPMachine LearningUI traversal
0 likes · 8 min read
Applying Reinforcement Learning to UI Traversal for Automated Testing
JD Tech Talk
JD Tech Talk
Feb 13, 2020 · Artificial Intelligence

Full-Process Traceability Management for Machine Learning Models: Challenges, Methods, and Solutions

This article analyzes the challenges of managing the entire machine‑learning lifecycle, reviews existing traceability approaches, and proposes comprehensive methods for versioned management of model training, prediction, and online service to improve efficiency, reproducibility, and maintenance of AI systems.

AI WorkflowMachine LearningModel deployment
0 likes · 18 min read
Full-Process Traceability Management for Machine Learning Models: Challenges, Methods, and Solutions
DataFunTalk
DataFunTalk
Feb 12, 2020 · Artificial Intelligence

Beike's Risk Control System: Leveraging Knowledge Graphs and Graph Analytics

The article details how Beike's Agent Cooperation Network employs a multi‑layered risk control framework built on large‑scale knowledge graphs, graph mining, and machine‑learning techniques to detect fake listings, malicious competition, and other threats across both online and offline real‑estate scenarios.

Knowledge GraphMachine Learninggraph analytics
0 likes · 10 min read
Beike's Risk Control System: Leveraging Knowledge Graphs and Graph Analytics
Big Data Technology Architecture
Big Data Technology Architecture
Feb 4, 2020 · Big Data

What Is a Data Lakehouse? Introduction, Key Features, and Evolution

The article explains the emerging Lakehouse paradigm that combines the low‑cost storage of data lakes with the management and ACID guarantees of data warehouses, detailing its advantages over traditional architectures, core capabilities, early implementations, and its role in supporting modern AI and analytics workloads.

AnalyticsLakehouseMachine Learning
0 likes · 9 min read
What Is a Data Lakehouse? Introduction, Key Features, and Evolution
DataFunTalk
DataFunTalk
Feb 3, 2020 · Artificial Intelligence

Alibaba Entertainment Search Algorithm Practice and Insights – Video Search Case Study with Youku

The live session presented Alibaba Entertainment’s senior algorithm expert discussing Youku’s video search business, relevance and ranking models, multimodal search challenges, and practical AI techniques, offering attendees a comprehensive view of modern video retrieval systems and their implementation.

AIMachine LearningMultimodal
0 likes · 3 min read
Alibaba Entertainment Search Algorithm Practice and Insights – Video Search Case Study with Youku
DataFunTalk
DataFunTalk
Jan 21, 2020 · Artificial Intelligence

How to Enhance Real-Time Updating of Recommendation System Models

The article examines various techniques—including full, incremental, online, and local updates—as well as client‑side embedding refreshes to improve the real‑time performance of recommendation system models, balancing freshness with global optimality.

AIMachine LearningRecommendation Systems
0 likes · 9 min read
How to Enhance Real-Time Updating of Recommendation System Models
Tencent Cloud Developer
Tencent Cloud Developer
Jan 21, 2020 · Artificial Intelligence

Cold-Start Short Video Potential Prediction Using Siamese Networks

The paper proposes a Siamese‑based PredictionNet that combines EfficientB3 image and VGGish audio features with user metrics to predict a HotValue score for newly uploaded short videos, using a margin loss with view‑value‑aware pair selection, enabling tiered cold‑start exposure that boosts overall platform efficiency.

Cold StartMachine LearningSiamese Network
0 likes · 9 min read
Cold-Start Short Video Potential Prediction Using Siamese Networks
Huajiao Technology
Huajiao Technology
Jan 21, 2020 · Artificial Intelligence

Overview of Ranking Algorithms in Recommendation Systems

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

Deep LearningMachine LearningRecommendation Systems
0 likes · 20 min read
Overview of Ranking Algorithms in Recommendation Systems
DataFunTalk
DataFunTalk
Jan 20, 2020 · Artificial Intelligence

The Second Half of Knowledge Graphs: Opportunities and Challenges

This comprehensive report analyzes the evolution of knowledge graphs, reviews achievements of the first half, and examines the challenges and opportunities of the emerging second half, highlighting shifts from large‑scale simple applications to complex, expert‑driven scenarios, and outlining strategies for representation, acquisition, and application in the era of big data and AI.

AIKnowledge GraphKnowledge engineering
0 likes · 30 min read
The Second Half of Knowledge Graphs: Opportunities and Challenges
360 Quality & Efficiency
360 Quality & Efficiency
Jan 17, 2020 · Artificial Intelligence

File Release Application Prediction Model Using GBDT

This article describes how a GBDT‑based prediction model was built to forecast file release application parameters such as volume ratio, target audience, and gray level, covering data collection, feature engineering, model training, service deployment, and practical considerations for handling bad cases.

GBDTMachine Learningdata preprocessing
0 likes · 8 min read
File Release Application Prediction Model Using GBDT
Top Architect
Top Architect
Jan 16, 2020 · Artificial Intelligence

A Survey of Neural Architecture Search: Search Spaces, Optimization Strategies, and Recent Results

This article surveys neural architecture search, classifying existing methods, describing common search spaces—including global and cell‑based designs—detailing optimization strategies such as reinforcement learning, evolutionary algorithms, surrogate models, one‑shot and differentiable approaches, and highlighting recent results and trends in the field.

Evolutionary AlgorithmsMachine LearningNAS
0 likes · 13 min read
A Survey of Neural Architecture Search: Search Spaces, Optimization Strategies, and Recent Results
ITPUB
ITPUB
Jan 8, 2020 · Artificial Intelligence

Latest AI & Tech News: Ant Financial Blockchain, Baidu AI Reorg, Lyft’s Flyte Platform

This roundup highlights recent tech headlines, including Ant Financial’s blockchain platform rollout, Baidu’s AI organization restructuring, Zhou Hongyi’s view on knowledge‑payment versus 5G, Megvii’s AI governance institute launch, and Lyft’s open‑source Flyte machine‑learning platform.

AIBlockchainIndustry Updates
0 likes · 5 min read
Latest AI & Tech News: Ant Financial Blockchain, Baidu AI Reorg, Lyft’s Flyte Platform
DataFunTalk
DataFunTalk
Jan 6, 2020 · Artificial Intelligence

Weibo O-Series Advertising System: Smart Bidding, Intelligent Targeting, and ROI Modeling

The article explains Weibo’s O‑Series advertising system, detailing its three‑part strategy of smart bidding, intelligent targeting, and ROI modeling, the underlying machine‑learning techniques such as deep‑FM, dual‑tower and PID control, and how these components optimize show, click, conversion rates and advertiser ROI.

AdvertisingMachine LearningROI
0 likes · 14 min read
Weibo O-Series Advertising System: Smart Bidding, Intelligent Targeting, and ROI Modeling
DataFunTalk
DataFunTalk
Jan 3, 2020 · Artificial Intelligence

Survey of Machine Learning Model Interpretability Techniques

This article provides a comprehensive survey of model interpretability in machine learning, covering its importance, evaluation criteria, and a wide range of techniques such as permutation importance, partial dependence plots, ICE, LIME, SHAP, RETAIN, and LRP, along with practical code examples and visualizations.

Feature ImportanceICELIME
0 likes · 39 min read
Survey of Machine Learning Model Interpretability Techniques
Amap Tech
Amap Tech
Jan 3, 2020 · Artificial Intelligence

Machine Learning Solutions for User Feedback Intelligence at Amap (Gaode Maps)

Amap replaced its rule‑based feedback pipeline with a three‑stage, LSTM‑driven system that combines word2vec embeddings and structured fields, achieving over 96% classification accuracy, cutting manual workload by 80%, and slashing per‑task costs while enabling scalable, data‑driven map quality improvements.

Gaode MapsLSTMMachine Learning
0 likes · 14 min read
Machine Learning Solutions for User Feedback Intelligence at Amap (Gaode Maps)
Alibaba Terminal Technology
Alibaba Terminal Technology
Jan 3, 2020 · Frontend Development

How Frontend Code Was Auto-Generated for Alibaba’s Double‑11 Event

This article explains how Alibaba's Frontend Intelligent Project automatically generated 79.34% of the Double‑11 page code by recognizing business modules from visual drafts using data‑augmented samples, traditional multi‑class machine‑learning models, and a pipeline of preprocessing, model training, deployment, and OOD handling.

Machine LearningModel deploymentautomation
0 likes · 15 min read
How Frontend Code Was Auto-Generated for Alibaba’s Double‑11 Event
DataFunTalk
DataFunTalk
Dec 30, 2019 · Artificial Intelligence

Technical Trends in Recommendation Systems: From Retrieval to Re‑ranking

This article surveys recent advances in recommendation system technology, covering the evolution from a two‑stage recall‑ranking pipeline to a four‑stage architecture, and detailing emerging trends in model‑based recall, user‑behavior sequence modeling, knowledge‑graph integration, graph neural networks, advanced ranking models, multi‑objective optimization, multimodal fusion, and listwise re‑ranking.

Knowledge GraphMachine LearningRecommendation Systems
0 likes · 45 min read
Technical Trends in Recommendation Systems: From Retrieval to Re‑ranking
Tencent Cloud Developer
Tencent Cloud Developer
Dec 26, 2019 · Artificial Intelligence

WeChat Scan-to-Identify (Scan Object) Feature: Overview, Technical Architecture, Data Construction, and Algorithmic Advances

WeChat’s iOS Scan‑to‑Identify feature lets users point a camera at any product or scene to instantly retrieve related e‑commerce, encyclopedia or news content, using a four‑pipeline architecture that builds massive annotated and deduplicated databases, advanced RetinaNet‑based detection, multi‑task metric learning, and scalable training, deployment and scheduling platforms, with plans to extend into domains like facial, vehicle and plant recognition.

AIMachine LearningWeChat
0 likes · 34 min read
WeChat Scan-to-Identify (Scan Object) Feature: Overview, Technical Architecture, Data Construction, and Algorithmic Advances
DataFunTalk
DataFunTalk
Dec 20, 2019 · Artificial Intelligence

AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications

The article presents AutoCross, a system that automatically generates and selects high‑order feature crossings for tabular data using multi‑granularity discretization, beam search, field‑wise logistic regression and successive mini‑batch gradient descent, achieving superior accuracy and efficiency in large‑scale recommendation scenarios.

AutoCrossMachine LearningRecommendation Systems
0 likes · 10 min read
AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications
MaGe Linux Operations
MaGe Linux Operations
Dec 20, 2019 · Fundamentals

Master 70 Essential NumPy Exercises: From Basics to Advanced Techniques

A comprehensive collection of 70 NumPy exercises with detailed problem statements, code snippets, and expected outputs that cover array creation, manipulation, statistical analysis, handling missing data, encoding, sorting, and advanced mathematical operations for Python developers.

Array OperationsMachine LearningNumPy
0 likes · 20 min read
Master 70 Essential NumPy Exercises: From Basics to Advanced Techniques
Bitu Technology
Bitu Technology
Dec 20, 2019 · Big Data

Building a Model‑Driven Data Platform at Tubi: From Data Warehouse to Automated Machine Learning

The article describes how Tubi, North America’s largest free‑streaming service, built a model‑driven data platform using a high‑quality data warehouse, DBT‑based transformations, Kubernetes‑hosted JupyterHub, low‑latency Scala/Akka services, and automated machine‑learning pipelines to accelerate experimentation and decision‑making.

Machine Learningdata engineeringdata platform
0 likes · 11 min read
Building a Model‑Driven Data Platform at Tubi: From Data Warehouse to Automated Machine Learning
360 Quality & Efficiency
360 Quality & Efficiency
Dec 20, 2019 · Artificial Intelligence

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

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

APK testingDeep LearningMachine Learning
0 likes · 14 min read
Automated APK Test Script Recommendation: Data Processing and Model Training Pipeline
Tencent Cloud Developer
Tencent Cloud Developer
Dec 19, 2019 · Artificial Intelligence

AI-Powered Content Moderation: How Platforms Combat Harmful Content with AI

AI-powered moderation tools now scan text, images, live streams, and short videos, using techniques like TextCNN, Word2Vec, attention‑based classifiers, multi‑label sampling, and real‑time audio analysis to detect pornographic and harmful content, while emphasizing continual model updates and sample collection for both small and large platforms.

AI detectionMachine LearningNatural Language Processing
0 likes · 12 min read
AI-Powered Content Moderation: How Platforms Combat Harmful Content with AI
Alibaba Cloud Developer
Alibaba Cloud Developer
Dec 17, 2019 · Artificial Intelligence

How Alibaba Boosted Short‑Video Engagement with Advanced Recommendation Algorithms

This article explains the rapid growth of short‑video on Taobao, describes the video feature framework, details the RankI2V and RankV2V recall methods, outlines coarse and fine ranking models, and presents real‑time interest and business strategies that significantly improved click‑through rates and viewing time.

AlibabaMachine Learningranking algorithms
0 likes · 19 min read
How Alibaba Boosted Short‑Video Engagement with Advanced Recommendation Algorithms
58 Tech
58 Tech
Dec 16, 2019 · Artificial Intelligence

Data Intelligence in the Used‑Car Business: User Traffic Prediction and Identification (Part 1)

This article details how the 58 Group applied data‑driven methods—user segmentation, interest description, clustering, and predictive modeling—to forecast and identify traffic in the used‑car scenario, illustrating the end‑to‑end pipeline, experimental results, and practical impact on downstream business processes.

Data IntelligenceMachine LearningTraffic Prediction
0 likes · 19 min read
Data Intelligence in the Used‑Car Business: User Traffic Prediction and Identification (Part 1)
DataFunTalk
DataFunTalk
Dec 13, 2019 · Artificial Intelligence

Fundamentals of Deep Learning: Neural Networks, CNNs, RNNs, LSTM, and GRU

This article provides a comprehensive overview of deep learning fundamentals, covering neural network basics, forward and backward feedback architectures, key models such as MLP, CNN, RNN, LSTM and GRU, training techniques like gradient descent, learning rate schedules, momentum, weight decay, and batch normalization.

CNNDeep LearningGRU
0 likes · 14 min read
Fundamentals of Deep Learning: Neural Networks, CNNs, RNNs, LSTM, and GRU
Alibaba Cloud Developer
Alibaba Cloud Developer
Dec 12, 2019 · Artificial Intelligence

How Multi‑Layer Multi‑Frequency Streaming Training Boosts Real‑Time CTR/CVR Prediction

This article details a novel Multi‑Layer Multi‑Frequency streaming training approach that enables minute‑level real‑time updates of massive CTR/CVR models by partitioning weights into freezing embeddings, changing embeddings, and changing weights, demonstrating significant offline and online AUC gains, especially during high‑traffic events like Double 11.

CTR predictionE‑commerceMachine Learning
0 likes · 18 min read
How Multi‑Layer Multi‑Frequency Streaming Training Boosts Real‑Time CTR/CVR Prediction
DataFunTalk
DataFunTalk
Dec 9, 2019 · Artificial Intelligence

Automatic Construction of Knowledge Graphs: Methods, Challenges, and Applications

This article reviews the principles, techniques, and challenges of automatically building knowledge graphs, covering logical modeling, latent‑space analysis, human‑computer interaction, ontology support, and practical pipelines, and illustrates their use in network behavior analysis, intelligent Q&A, and recommendation systems.

Artificial IntelligenceKnowledge GraphMachine Learning
0 likes · 17 min read
Automatic Construction of Knowledge Graphs: Methods, Challenges, and Applications
Taobao Frontend Technology
Taobao Frontend Technology
Dec 5, 2019 · Frontend Development

From UI Sketch to Code: Frontend Intelligence Generates 79% of Double‑11 Modules

This article explains how Alibaba's Front‑End Intelligent project automatically converts UI design images into production‑ready code, covering layout analysis, background and foreground processing, a fusion of traditional image algorithms with deep‑learning detection, GAN‑based complex‑background extraction, experimental results and real‑world deployment.

GaNMachine Learningautomation
0 likes · 21 min read
From UI Sketch to Code: Frontend Intelligence Generates 79% of Double‑11 Modules
Alibaba Terminal Technology
Alibaba Terminal Technology
Dec 5, 2019 · Frontend Development

How Frontend Code Is Automatically Generated: Inside Alibaba’s AI‑Powered D2C Pipeline

This article explains Alibaba's front‑end intelligent project that automatically generated 79.34% of the Double‑11 UI code, detailing why images are used as input, the layered image‑processing pipeline, background and foreground analysis, traditional versus deep‑learning methods, fusion techniques, evaluation results, and real‑world deployments.

Machine Learningcode generationfrontend
0 likes · 20 min read
How Frontend Code Is Automatically Generated: Inside Alibaba’s AI‑Powered D2C Pipeline
Tencent Cloud Developer
Tencent Cloud Developer
Dec 3, 2019 · Artificial Intelligence

Feature Engineering Practices for Short‑Video Recommendation Systems

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

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

Ranking Strategy Optimization Practices for Commercial Traffic at 58.com

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

Machine LearningReal-time Data Pipelinefeature engineering
0 likes · 17 min read
Ranking Strategy Optimization Practices for Commercial Traffic at 58.com
Amap Tech
Amap Tech
Nov 29, 2019 · Artificial Intelligence

Advancements in Query Analysis for Map Search: City Analysis, Where‑What Segmentation, and Path Planning

The article details Amap’s upgraded map‑search query analysis, introducing a two‑stage city‑identification system, enhanced where‑what segmentation with CRF and GBDT models, a three‑stage path‑planning pipeline, and outlines future deep‑learning and knowledge‑graph enhancements for robustness and low‑frequency query handling.

Machine LearningMap SearchNLP
0 likes · 14 min read
Advancements in Query Analysis for Map Search: City Analysis, Where‑What Segmentation, and Path Planning
DataFunTalk
DataFunTalk
Nov 28, 2019 · Artificial Intelligence

Web Data Mining and Page Analysis Techniques for Search Engines

This article explains how search engines collect, analyze, and rank web pages by describing the spider system, HTML and layout tree construction, feature extraction, and machine‑learning based classification methods used to understand page content and improve result relevance.

HTML treeMachine Learningfeature extraction
0 likes · 8 min read
Web Data Mining and Page Analysis Techniques for Search Engines
Tianxing Digital Tech User Experience
Tianxing Digital Tech User Experience
Nov 22, 2019 · Product Management

Can Tesla’s Shadow‑Mode Revolutionize Product Design Evaluation?

This article examines the shortcomings of traditional usability testing, explains Tesla’s shadow‑mode data collection and high‑precision mapping, and proposes how the same AI‑driven, data‑rich approach can be adapted to create a self‑learning, automated product‑design evaluation and iteration cycle.

AIBig DataMachine Learning
0 likes · 14 min read
Can Tesla’s Shadow‑Mode Revolutionize Product Design Evaluation?