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1966 articles · Page 7 of 20
Test Development Learning Exchange
Test Development Learning Exchange
Sep 12, 2023 · Artificial Intelligence

Various Anomaly Detection Techniques with Python Code Examples

This article introduces ten common anomaly detection approaches—including statistical thresholds, boxplots, clustering, isolation forest, LOF, collaborative filtering, robust covariance, NLP, computer‑vision, and time‑series methods—each accompanied by concise Python code snippets illustrating how to identify outliers in different data domains.

ClusteringPythonTime Series
0 likes · 9 min read
Various Anomaly Detection Techniques with Python Code Examples
Open Source Linux
Open Source Linux
Sep 8, 2023 · Artificial Intelligence

How ChatGPT Works: Inside the Neural Network That Generates Human‑Like Text

This article explains the inner workings of ChatGPT, covering how large language models predict the next token using probability distributions, the role of embeddings, the transformer architecture with attention heads, training methods, loss functions, and why such a massive neural network can produce coherent, human‑like language.

ChatGPTTransformerembeddings
0 likes · 79 min read
How ChatGPT Works: Inside the Neural Network That Generates Human‑Like Text
Alimama Tech
Alimama Tech
Sep 6, 2023 · Artificial Intelligence

Learning-Based Ad Auction Design with Externalities: Score-Weighted VCG Framework

The paper introduces Score‑Weighted VCG, a learning‑based ad auction framework that models externalities by learning a monotone scoring function and solving a weighted‑welfare matching problem, achieving incentive compatibility, individual rationality, and near‑optimal revenue and welfare on synthetic and large‑scale Taobao data.

Ad AuctionExternalitiesVCG
0 likes · 16 min read
Learning-Based Ad Auction Design with Externalities: Score-Weighted VCG Framework
DataFunSummit
DataFunSummit
Sep 3, 2023 · Artificial Intelligence

Estimating Clustered Data Causal Effects with DiConfounder: A Double‑Difference Framework

This article presents a comprehensive approach to estimating causal effects on clustered data using a double‑difference method, introduces the DiConfounder algorithm built on Rubin Causal Model extensions, details data characteristics, model assumptions, six‑step pipeline, and reports competitive results on the ACIC2022 challenge.

Causal InferenceDiConfounderclustered data
0 likes · 13 min read
Estimating Clustered Data Causal Effects with DiConfounder: A Double‑Difference Framework
DataFunSummit
DataFunSummit
Sep 1, 2023 · Artificial Intelligence

Observational Causal Inference and De‑Confounding Techniques for Industrial Applications

This article introduces the fundamentals of causal inference from observational data, explains confounding and the SUTVA assumptions, presents the do‑operator, and details four de‑confounding strategies—including RCT‑based resampling, feature‑decomposition, double machine learning, and back‑/front‑door adjustments—followed by real‑world applications in recommendation systems and resource allocation.

Causal Inferencedeconfoundingmachine learning
0 likes · 22 min read
Observational Causal Inference and De‑Confounding Techniques for Industrial Applications
Model Perspective
Model Perspective
Aug 31, 2023 · Artificial Intelligence

Master Feature Selection: From Filters to PCA with Python

This article explains why selecting the right features is essential for machine learning, outlines the general workflow, compares filter, wrapper, and embedded methods, demonstrates statistical tests and Python code examples, and shows how PCA can synthesize features for dimensionality reduction.

PCAPythonchi-square
0 likes · 18 min read
Master Feature Selection: From Filters to PCA with Python
Model Perspective
Model Perspective
Aug 30, 2023 · Artificial Intelligence

How Gradient Descent Trains Neural Networks: A Blind Hiker’s Journey

This article uses a blindfolded mountain‑climbing analogy to explain how gradient descent trains neural networks, covering cost functions, learning rates, iterative updates, and provides a Python implementation for a simple three‑layer network example.

AIBackpropagationNeural Network
0 likes · 10 min read
How Gradient Descent Trains Neural Networks: A Blind Hiker’s Journey
Model Perspective
Model Perspective
Aug 26, 2023 · Artificial Intelligence

75 Essential Data Science Terms Every Practitioner Must Know

This article compiles a comprehensive alphabetically ordered list of 75 crucial data science and machine learning terms—from accuracy and AUC to zero-shot learning—providing concise definitions that help practitioners quickly grasp essential concepts and improve their analytical vocabulary.

AI termsData ScienceGlossary
0 likes · 13 min read
75 Essential Data Science Terms Every Practitioner Must Know
Model Perspective
Model Perspective
Aug 26, 2023 · Artificial Intelligence

Why Accuracy Isn’t Enough: Mastering MCC for Imbalanced Classification

This article reviews common classification evaluation metrics—accuracy, precision, recall, and F1—explains their limitations on imbalanced data, and introduces the Matthews Correlation Coefficient (MCC) with Python implementations to provide a more reliable performance measure.

ClassificationMCCPython
0 likes · 5 min read
Why Accuracy Isn’t Enough: Mastering MCC for Imbalanced Classification
dbaplus Community
dbaplus Community
Aug 26, 2023 · Databases

What Is a Vector Database? A Simple Guide from Kids to Engineers

This article demystifies vector databases by first explaining the concept with a five‑year‑old analogy, then expanding to technical details for developers, covering how embeddings work, the differences from relational databases, ANN search, indexing, similarity metrics, and why vector stores outperform raw NumPy arrays for large‑scale similarity retrieval.

ANNdatabasesmachine learning
0 likes · 9 min read
What Is a Vector Database? A Simple Guide from Kids to Engineers
Model Perspective
Model Perspective
Aug 25, 2023 · Artificial Intelligence

Understanding Common Loss Functions Across Machine Learning Models

This article explains the purpose of loss functions in machine learning and reviews the specific loss functions used by popular algorithms such as linear regression (MSE), logistic regression (cross‑entropy), decision trees, random forests, SVM (hinge loss), neural networks, and AdaBoost (exponential loss).

AIAlgorithmsLoss Functions
0 likes · 3 min read
Understanding Common Loss Functions Across Machine Learning Models
DaTaobao Tech
DaTaobao Tech
Aug 25, 2023 · Industry Insights

Why AI Engineers Are the Next Hot Tech Role—and How to Become One

The article examines the rapid rise of AI engineers, defines their responsibilities, compares them with traditional ML engineers, analyzes market demand and challenges, and outlines practical steps for aspiring professionals to acquire the skills and experience needed for this emerging role.

AI EngineerCareerindustry trends
0 likes · 17 min read
Why AI Engineers Are the Next Hot Tech Role—and How to Become One
Model Perspective
Model Perspective
Aug 24, 2023 · Fundamentals

Master Essential Data Visualization Techniques for Data Science

This article presents a comprehensive collection of practical data visualization methods—including KS plots, SHAP explanations, Q‑Q plots, cumulative variance, Gini vs Entropy, bias‑variance tradeoff, ROC and precision‑recall curves, and elbow analysis—each illustrated with Python code and clear explanations to help analysts and non‑experts quickly interpret complex datasets.

Data Visualizationmachine learningplotting
0 likes · 25 min read
Master Essential Data Visualization Techniques for Data Science
JD Cloud Developers
JD Cloud Developers
Aug 22, 2023 · Artificial Intelligence

A Practical Guide to Recommendation System Architecture and Methods

This article provides a concise overview of recommendation systems, covering their definition, core framework of recall, ranking, and re‑ranking, various recall strategies including multi‑path and vector‑based methods, similarity calculations, and practical implementation details such as AB testing and code examples.

AB TestingInformation RetrievalRecall
0 likes · 14 min read
A Practical Guide to Recommendation System Architecture and Methods
Ele.me Technology
Ele.me Technology
Aug 21, 2023 · Artificial Intelligence

Exploring Spatiotemporal Features and Adaptive Context Modeling for Online Food Recommendation (DCAM)

The paper introduces DCAM, a dynamic context‑adaptation model that automatically selects the most effective spatiotemporal features for online food recommendation, showing that more features or naïve self‑attention do not guarantee gains, and achieving superior offline AUC and online CTR improvements over existing state‑of‑the‑art methods.

DCAMSpatiotemporalcontext adaptation
0 likes · 13 min read
Exploring Spatiotemporal Features and Adaptive Context Modeling for Online Food Recommendation (DCAM)
Python Crawling & Data Mining
Python Crawling & Data Mining
Aug 20, 2023 · Artificial Intelligence

What Is RLHF? Benefits, Limits, and Design Tips for Human‑Feedback Reinforcement Learning

This article explains Reinforcement Learning with Human Feedback (RLHF), outlining its definition, suitable tasks, advantages over other reward‑model methods, types of algorithms, challenges of human feedback, and practical strategies to mitigate its limitations for building robust AI systems.

AI AlignmentHuman FeedbackReward Modeling
0 likes · 14 min read
What Is RLHF? Benefits, Limits, and Design Tips for Human‑Feedback Reinforcement Learning
Model Perspective
Model Perspective
Aug 19, 2023 · Artificial Intelligence

Unlocking Hidden Patterns: How Tensor Decomposition Powers Modern AI

This article introduces tensors and tensor decomposition, explains core operations, explores CP and other factorization methods, and demonstrates Python implementations for music and movie recommendation systems, highlighting how these techniques reveal hidden structures in large‑scale data.

CP decompositionPythonbig data
0 likes · 15 min read
Unlocking Hidden Patterns: How Tensor Decomposition Powers Modern AI
JD Retail Technology
JD Retail Technology
Aug 18, 2023 · Artificial Intelligence

Overview of Recommendation Systems: Definitions, Architecture, Recall, Ranking, and Re‑ranking

This article provides a comprehensive overview of recommendation systems, covering their definition, basic framework, request flow, AB testing, recall strategies (both non‑personalized and personalized), collaborative‑filtering methods, vector‑based retrieval, wide‑and‑deep models, and the MMR re‑ranking algorithm with code examples.

RecallVector Retrievalcollaborative filtering
0 likes · 14 min read
Overview of Recommendation Systems: Definitions, Architecture, Recall, Ranking, and Re‑ranking
Model Perspective
Model Perspective
Aug 17, 2023 · Artificial Intelligence

Can Math Build the Ultimate Pokémon Dream Team? A Data‑Driven Analysis

This article uses a Kaggle Pokémon dataset of 802 creatures to explore statistical correlations, build a random‑forest classifier for legendary status, assess type strengths, and apply optimization techniques—including integer linear programming, greedy selection, and simulated annealing—to propose an optimal six‑Pokémon dream team.

Data AnalysisOptimizationmachine learning
0 likes · 23 min read
Can Math Build the Ultimate Pokémon Dream Team? A Data‑Driven Analysis
Ele.me Technology
Ele.me Technology
Aug 17, 2023 · Artificial Intelligence

BASM: A Bottom‑up Adaptive Spatiotemporal Model for Online Food Ordering Service

BASM is a bottom‑up adaptive spatiotemporal model for online food ordering that uses hierarchical embedding, semantic transformation, and adaptive bias layers to dynamically modulate parameters according to time and location, thereby capturing multiple data distributions and achieving superior offline metrics and online A/B test performance.

CTR predictionadaptive parametersmachine learning
0 likes · 18 min read
BASM: A Bottom‑up Adaptive Spatiotemporal Model for Online Food Ordering Service
Baidu Geek Talk
Baidu Geek Talk
Aug 16, 2023 · Artificial Intelligence

Understanding Reinforcement Learning: From Basics to PPO and Policy Gradient

This article provides a comprehensive overview of reinforcement learning, covering fundamental concepts, differences from supervised learning, algorithm families, policy gradient methods, practical tricks like baselines and reward‑to‑go, and detailed explanations of TRPO and PPO variants with illustrative diagrams.

PPOactor-criticmachine learning
0 likes · 19 min read
Understanding Reinforcement Learning: From Basics to PPO and Policy Gradient
Model Perspective
Model Perspective
Aug 13, 2023 · Artificial Intelligence

Unlocking Hidden Markov Models: Theory, Algorithms, and Python Implementations

This article explains Hidden Markov Models, covering their core concepts, basic elements, the three fundamental problems with forward, Viterbi, and Baum‑Welch algorithms, provides a weather illustration, detailed Python code using hmmlearn, and a real‑world earthquake case study, highlighting practical implementation steps.

HMMHidden Markov ModelPython
0 likes · 15 min read
Unlocking Hidden Markov Models: Theory, Algorithms, and Python Implementations
Meituan Technology Team
Meituan Technology Team
Aug 10, 2023 · Artificial Intelligence

Selected Meituan Technical Papers from KDD 2023: Summaries of Seven Research Works

The article showcases seven Meituan research papers accepted at KDD 2023—spanning feed‑stream, cross‑domain, takeaway, bonus allocation, contour‑based segmentation, living‑needs prediction, and multilingual recommendation—detailing their novel methods, real‑world deployments, and concluding with an invitation for academic collaboration.

Artificial IntelligenceKDD 2023Meituan
0 likes · 17 min read
Selected Meituan Technical Papers from KDD 2023: Summaries of Seven Research Works
AntTech
AntTech
Aug 8, 2023 · Artificial Intelligence

AIGC Reshapes the Financial Service Chain, Driving New Efficiency and Ecosystem

The 4th China AI Competition results conference highlighted how generative AI (AIGC) is being integrated into Ant Group's financial services to automate workflows, enhance marketing creativity, and improve claim processing, promising exponential efficiency gains, new customer experiences, and a transformed industry ecosystem.

AIGCautomationbusiness efficiency
0 likes · 7 min read
AIGC Reshapes the Financial Service Chain, Driving New Efficiency and Ecosystem
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Aug 6, 2023 · Artificial Intelligence

Explaining Image Recognition: Logistic Regression and Convolutional Neural Networks

This article introduces the principles of image recognition, compares traditional logistic regression with convolutional neural networks, demonstrates their implementation using Python code, visualizes model weights, and explains key concepts such as padding, convolution, pooling, receptive fields, and multi‑layer feature extraction.

Logistic Regressionconvolutional neural networkexplainable AI
0 likes · 12 min read
Explaining Image Recognition: Logistic Regression and Convolutional Neural Networks
360 Quality & Efficiency
360 Quality & Efficiency
Aug 4, 2023 · Artificial Intelligence

Machine Learning Model Testing Workflow and Best Practices

This article outlines the essential concepts, data preparation, model creation, training, deployment, and verification steps for testing machine‑learning models, highlighting dataset requirements, algorithm categories, framework choices, resource considerations, and provides a sample inference request.

AIXGBoostdata preparation
0 likes · 7 min read
Machine Learning Model Testing Workflow and Best Practices
Kuaishou Tech
Kuaishou Tech
Aug 4, 2023 · Artificial Intelligence

Highlights of Six KDD 2023 Papers on Personalized Recommendation and User Behavior Modeling

This article summarizes six KDD 2023 research papers—PEPNet, TWIN, TPM, GFN4Rec, PrefRec, and GACN—detailing their download links, authors, and abstracts, which introduce novel personalized networks, two‑stage lifelong behavior modeling, tree‑based regression, generative flow recommendation, preference‑driven reinforcement learning, and graph adversarial contrastive learning for recommendation systems.

KDD2023machine learning
0 likes · 13 min read
Highlights of Six KDD 2023 Papers on Personalized Recommendation and User Behavior Modeling
Airbnb Technology Team
Airbnb Technology Team
Aug 3, 2023 · Artificial Intelligence

Improving Airbnb Search Ranking Diversity with Neural Networks

Airbnb upgraded its neural‑network ranking system by adding a similarity network that penalizes duplicate‑like listings, enabling the algorithm to present a more diverse set of options, which boosted booking rates, value, and five‑star ratings, demonstrating that reduced result similarity improves overall search quality.

AirbnbNeural Networkdiversity
0 likes · 8 min read
Improving Airbnb Search Ranking Diversity with Neural Networks
Bitu Technology
Bitu Technology
Aug 2, 2023 · Artificial Intelligence

Tubi's Recall Exploration: Embedding‑Based Candidate Generation for Scalable Video Recommendations

This article details Tubi's multi‑stage recommendation system, focusing on the recall phase and describing how popularity metrics, embedding averaging, per‑video nearest‑neighbors, hierarchical clustering, real‑time ranking, and context‑aware sampling are combined to efficiently generate personalized video candidates at scale.

EmbeddingRecallVideo Streaming
0 likes · 10 min read
Tubi's Recall Exploration: Embedding‑Based Candidate Generation for Scalable Video Recommendations
php Courses
php Courses
Aug 2, 2023 · Artificial Intelligence

Stanford and UC Berkeley Study Finds Significant Decline in GPT-4 Capabilities Across Math, Coding, and Visual Reasoning

A joint Stanford and UC Berkeley study reveals that GPT‑4’s performance on mathematics, code generation, and visual‑reasoning tasks sharply declined between March and June 2023, with accuracy dropping from 97.6% to 2.4% on a prime‑checking benchmark and executable code rates falling from 52% to 10%.

AI evaluationGPT-4Model Performance
0 likes · 3 min read
Stanford and UC Berkeley Study Finds Significant Decline in GPT-4 Capabilities Across Math, Coding, and Visual Reasoning
HomeTech
HomeTech
Aug 2, 2023 · Artificial Intelligence

Push Precision Recommendation System: Overview, Iteration, and Design

This article presents a comprehensive overview of the push precision recommendation system, detailing its data processing pipeline, machine‑learning‑driven algorithms, modular architecture—including offline, near‑real‑time, and push layers—and subsequent system iterations, optimizations, visual monitoring platforms, and future development directions.

Architecturebig datamachine learning
0 likes · 11 min read
Push Precision Recommendation System: Overview, Iteration, and Design
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Jul 29, 2023 · Artificial Intelligence

Introduction to Machine Learning: Concepts, Terminology, Algorithms, Evaluation Metrics, and Practical Code Examples

This article provides a comprehensive overview of machine learning, covering fundamental concepts, key terminology, common algorithms for supervised, unsupervised, and reinforcement learning, model evaluation metrics, loss functions, and practical code examples such as random forest and SVM implementations.

AlgorithmsLoss FunctionsUnsupervised Learning
0 likes · 35 min read
Introduction to Machine Learning: Concepts, Terminology, Algorithms, Evaluation Metrics, and Practical Code Examples
DataFunTalk
DataFunTalk
Jul 28, 2023 · Artificial Intelligence

Insurance Anti‑Fraud Risk Control System: Architecture, Core Capabilities, and Case Studies

This article presents Taiping Jinke's end‑to‑end insurance anti‑fraud risk control framework, detailing industry pain points, core AI‑driven capabilities, platform blueprint, specific car and health insurance fraud engines, and real‑world case studies that illustrate how big‑data, machine‑learning and knowledge‑graph techniques are integrated into business processes.

Knowledge Graphfraud detectionmachine learning
0 likes · 16 min read
Insurance Anti‑Fraud Risk Control System: Architecture, Core Capabilities, and Case Studies
Model Perspective
Model Perspective
Jul 27, 2023 · Fundamentals

Unlocking Markov Chains: From Weather Forecasts to Keyboard Predictions

This article introduces Markov chains as a mathematical model of state transitions, explains definitions, transition matrices, n‑step and steady‑state distributions, and demonstrates practical Python simulations for weather forecasting and simple keyboard word prediction.

Markov chainPythonSimulation
0 likes · 7 min read
Unlocking Markov Chains: From Weather Forecasts to Keyboard Predictions
Smart Era Software Development
Smart Era Software Development
Jul 26, 2023 · Artificial Intelligence

Three Core Skills Every Aspiring AI Architect Needs

The article defines the AI architect role and outlines three essential capabilities—mastery of AI technologies and development workflow, deep business understanding with strong abstraction ability, and the design and implementation of efficient, scalable AI solutions—explaining why each is critical for successful AI product delivery.

AI architectureBusiness AnalysisSystem Design
0 likes · 10 min read
Three Core Skills Every Aspiring AI Architect Needs
Bilibili Tech
Bilibili Tech
Jul 25, 2023 · Artificial Intelligence

Bilibili Game Center Recommendation System: Architecture, Core Technologies, and Experimental Results

The Bilibili Game Center recommendation system combines a unified feature platform, multi‑stage recall, ranking and re‑ranking models, online services, and AB experimentation to deliver personalized game suggestions, resulting in up to 78% higher click‑through, 76% higher conversion, and substantial increases in user engagement and revenue.

AB TestingGame Platformfeature engineering
0 likes · 26 min read
Bilibili Game Center Recommendation System: Architecture, Core Technologies, and Experimental Results
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Jul 24, 2023 · Artificial Intelligence

NetShare: An End-to-End System for GAN-Based IP Header Trace Packet Generation

This article presents NetShare, an end-to-end framework that uses time‑series GANs combined with domain‑specific encoding to synthesize privacy‑preserving IP header and flow traces, achieving up to 46% higher accuracy than prior generative baselines while improving the fidelity‑privacy trade‑off.

GaNIP Header TracingNetShare
0 likes · 7 min read
NetShare: An End-to-End System for GAN-Based IP Header Trace Packet Generation
Programmer DD
Programmer DD
Jul 20, 2023 · Artificial Intelligence

Why ChatGPT Mirrors Human Thought: Insights from Stephen Wolfram

ChatGPT, built on massive text training and simple neural network operations, generates human-like language yet lacks true understanding, prompting integration with Wolfram|Alpha’s precise computational language—a synergy highlighted by Stephen Wolfram’s insights on language structure, AI limits, and future computational possibilities.

Artificial IntelligenceChatGPTComputational Language
0 likes · 13 min read
Why ChatGPT Mirrors Human Thought: Insights from Stephen Wolfram
DataFunTalk
DataFunTalk
Jul 18, 2023 · Artificial Intelligence

Travel Demand Prediction and Recommendation Optimization at Fliggy: Challenges, Algorithm Evolution, and Future Directions

This article presents Fliggy's work on user travel demand prediction, outlining the unique challenges of travel scenarios, the evolution of recall and ranking algorithms—including multi‑task learning, graph‑based models, and intention‑capture mechanisms—and discusses future research directions such as long‑sequence modeling and cross‑domain learning.

Graph Neural Networksmachine learningmulti-task learning
0 likes · 19 min read
Travel Demand Prediction and Recommendation Optimization at Fliggy: Challenges, Algorithm Evolution, and Future Directions
DataFunTalk
DataFunTalk
Jul 16, 2023 · Artificial Intelligence

Application of Graph Neural Networks in Recommendation Systems: OPPO Business Scenario Practice

This article introduces graph neural networks, explains graph representation learning, discusses their evolution from random walks to spectral and spatial convolutions, and details how OPPO applies GNNs to improve recommendation system recall and ranking, highlighting practical architecture, experimental gains, and future research directions.

Graph Neural NetworksOPPOgraph representation learning
0 likes · 19 min read
Application of Graph Neural Networks in Recommendation Systems: OPPO Business Scenario Practice
DataFunSummit
DataFunSummit
Jul 14, 2023 · Artificial Intelligence

Iterative Evolution of JD Search EE System: Adaptive Exploration, Scenario Modeling, Scoring‑Insertion Consistency, and Context‑Aware Brand Store Detection

This article details the multi‑stage evolution of JD's search Explore‑Exploit (EE) system—covering an adaptive dynamic detection model, scenario‑modeling upgrades, end‑to‑end scoring and insertion consistency, and context‑aware brand/store dimension detection—demonstrating how each iteration improves result diversity, user experience, and key online metrics while maintaining search efficiency.

adaptive modelingexplore‑exploite‑commerce
0 likes · 24 min read
Iterative Evolution of JD Search EE System: Adaptive Exploration, Scenario Modeling, Scoring‑Insertion Consistency, and Context‑Aware Brand Store Detection
DataFunTalk
DataFunTalk
Jul 13, 2023 · Artificial Intelligence

Time Series Forecasting for NIO Power Swap Stations: Business Background, Challenges, and Algorithm Practice

This article presents NIO's smart energy service platform, focusing on the NIO Power swap‑station business and detailing how time‑series forecasting is applied to predict demand, addressing complex seasonality, holiday drift, growth and competition, and describing the underlying machine‑learning and deep‑learning models and system architecture.

EmbeddingNIOTime Series Forecasting
0 likes · 16 min read
Time Series Forecasting for NIO Power Swap Stations: Business Background, Challenges, and Algorithm Practice
DataFunTalk
DataFunTalk
Jul 12, 2023 · Artificial Intelligence

Evolution of Search EE System: Adaptive Exploration, Scenario Modeling, End-to-End Scoring Consistency, and Context-Aware Brand Store Detection

This article outlines the recent full‑cycle iterations of JD’s search Explore‑Exploit (EE) system, covering adaptive dynamic detection models, upgraded scenario modeling, two‑stage scoring and insertion consistency, end‑to‑end dynamic insertion, and context‑aware brand‑store dimension detection, with detailed methodology, experiments, and online results.

e-commerceexplore‑exploitmachine learning
0 likes · 22 min read
Evolution of Search EE System: Adaptive Exploration, Scenario Modeling, End-to-End Scoring Consistency, and Context-Aware Brand Store Detection
Architects' Tech Alliance
Architects' Tech Alliance
Jul 11, 2023 · Artificial Intelligence

Wear-Updated Integrated Feature Ranking (WEFR) for Robust SSD Failure Prediction

The article presents a large‑scale study of SSD failure prediction using SMART logs from multiple vendors, introduces the Wear‑Updated Integrated Feature Ranking (WEFR) method to automatically and robustly select predictive features, and demonstrates its effectiveness through extensive experiments on real‑world data.

SSDStorage ReliabilityWEFR
0 likes · 10 min read
Wear-Updated Integrated Feature Ranking (WEFR) for Robust SSD Failure Prediction
DataFunSummit
DataFunSummit
Jul 10, 2023 · Artificial Intelligence

Applying Causal Inference to Business Improvement: Concepts, Methods, and Case Studies from Xiaohongshu

This article explains why causal inference is needed in data‑driven businesses, introduces its theoretical foundations from computer science, econometrics and statistics, and demonstrates how various causal modeling techniques can be used to boost user retention and content creation on the Xiaohongshu platform.

A/B testingBusiness AnalyticsCausal Inference
0 likes · 12 min read
Applying Causal Inference to Business Improvement: Concepts, Methods, and Case Studies from Xiaohongshu
php Courses
php Courses
Jul 3, 2023 · Databases

Top 5 Revolutionary Vector Databases Transforming Machine Learning and Similarity Search (2023)

Vector databases store and search large-scale vector data, and in 2023 the five leading solutions—Chroma, Pinecone, Weaviate, Milvus, and Faiss—offer scalable, high-performance options for applications such as LLM-driven services, audio search, recommendation systems, image/video analysis, and semantic retrieval across various industries.

AIData StorageLLM
0 likes · 4 min read
Top 5 Revolutionary Vector Databases Transforming Machine Learning and Similarity Search (2023)
php Courses
php Courses
Jun 30, 2023 · Artificial Intelligence

Notable Real-World Failures of Data and Machine Learning Algorithms Over the Past Decade

Over the past decade, numerous high‑profile incidents have shown that flawed data and machine‑learning algorithms can cause severe consequences, from legal mishaps with ChatGPT to biased medical diagnoses, inaccurate real‑estate pricing, and discriminatory hiring practices, underscoring the need for rigorous data validation and algorithmic fairness.

AI ethicsCase StudiesData Quality
0 likes · 3 min read
Notable Real-World Failures of Data and Machine Learning Algorithms Over the Past Decade
Alimama Tech
Alimama Tech
Jun 28, 2023 · Artificial Intelligence

Historical Data Reuse for Precise CVR Prediction during E‑commerce Promotions

Alibaba’s Advertising Ranking team introduced the Historical Data Reuse (HDR) algorithm, which automatically selects similar past promotion days, fine‑tunes the production CVR model with a TransBlock layer and distribution‑correction weighting, delivering up to 10 % AUC gains and double‑digit RPM, CVR, and ROI improvements during the 2022 Double‑11 event and offering a reusable solution for other domains facing abrupt user‑behavior shifts.

conversion rate predictiondistribution shifthistorical data reuse
0 likes · 30 min read
Historical Data Reuse for Precise CVR Prediction during E‑commerce Promotions
Python Programming Learning Circle
Python Programming Learning Circle
Jun 28, 2023 · Artificial Intelligence

A Comprehensive Overview of Common Python Libraries for Artificial Intelligence

This article provides a concise yet comprehensive introduction to popular Python libraries for artificial intelligence, including NumPy, OpenCV, scikit-image, Pillow, SimpleCV, Mahotas, Ilastik, scikit-learn, SciPy, NLTK, spaCy, LibROSA, Pandas, Matplotlib, Seaborn, Orange, PyBrain, Milk, TensorFlow, PyTorch, Theano, Keras, Caffe, MXNet, PaddlePaddle, and CNTK, and demonstrates their basic usage with code examples.

AILibrariesmachine learning
0 likes · 33 min read
A Comprehensive Overview of Common Python Libraries for Artificial Intelligence
DataFunTalk
DataFunTalk
Jun 28, 2023 · Artificial Intelligence

Building and Applying a Multi‑Language Product Knowledge Graph at Shopee

This presentation details Shopee's approach to constructing a multilingual product knowledge graph, covering ontology modeling, data acquisition, fusion techniques, and practical applications, while discussing challenges, model architectures, and future directions for large‑scale e‑commerce AI systems.

Knowledge Graphe-commercemachine learning
0 likes · 20 min read
Building and Applying a Multi‑Language Product Knowledge Graph at Shopee
IT Services Circle
IT Services Circle
Jun 26, 2023 · Databases

Understanding Vector Databases and Embedding Techniques

The article explains what vector databases are, how vectors and embeddings work, the main embedding methods such as matrix factorization, NLP and graph techniques, the characteristics and high‑availability requirements of vector databases, and common AI‑driven application scenarios like semantic search, recommendation and anomaly detection.

AIEmbeddingmachine learning
0 likes · 8 min read
Understanding Vector Databases and Embedding Techniques
Alimama Tech
Alimama Tech
Jun 21, 2023 · Artificial Intelligence

Joint Optimization of Ranking and Calibration (JRC) for CTR Prediction

The Joint Optimization of Ranking and Calibration (JRC) model introduces a two‑logit generative‑discriminative architecture that jointly minimizes LogLoss for calibration and a listwise ranking loss, delivering superior GAUC and CTR performance across Alibaba’s display‑ad system, especially for sparse long‑tail users, while remaining simple to train and deploy.

CTR predictionHybrid Modelcalibration
0 likes · 18 min read
Joint Optimization of Ranking and Calibration (JRC) for CTR Prediction
DataFunSummit
DataFunSummit
Jun 18, 2023 · Artificial Intelligence

Generalized Causal Forest: Construction and Application in Online Trading Markets

This article introduces the generalized causal forest, explains its non‑parametric nonlinear construction for estimating heterogeneous dose‑response functions, compares it with existing methods, and demonstrates its experimental results and deployment in an online ride‑hailing pricing system to balance supply and demand.

Causal InferenceGeneralized Causal Forestheterogeneous treatment effect
0 likes · 7 min read
Generalized Causal Forest: Construction and Application in Online Trading Markets
DataFunSummit
DataFunSummit
Jun 18, 2023 · Artificial Intelligence

Intelligent Risk Control Forum – Sessions on Graph Algorithms, Pre‑trained GNN, Loop Detection, Active Learning, and Unstructured Data

The Intelligent Risk Control Forum gathers experts from Tencent, Huawei, Ant Group and academia to present the latest research on graph‑based algorithms, loop detection, pre‑trained graph neural networks, active learning and unstructured‑data risk models, addressing challenges such as data sparsity, adversarial behavior and model robustness.

Graph AlgorithmsLoop Detectionmachine learning
0 likes · 8 min read
Intelligent Risk Control Forum – Sessions on Graph Algorithms, Pre‑trained GNN, Loop Detection, Active Learning, and Unstructured Data
Java Architecture Diary
Java Architecture Diary
Jun 16, 2023 · Backend Development

Unlock Faster Java: Oracle GraalVM Native Image’s Startup, Memory, and Throughput Gains

Oracle GraalVM’s new release for JDK 17 and JDK 20 adds free‑to‑use native‑image features—including profile‑guided optimizations, G1 GC, object‑header compression, ML‑driven PGO, and SBOM support—delivering up to 46% faster startup, 2‑3× lower memory usage, and up to 1.6× higher peak throughput compared with traditional JIT, while also introducing new tooling such as native‑image bundles, build reports, enhanced AWT support, and experimental monitoring.

GraalVMJava performanceNative Image
0 likes · 18 min read
Unlock Faster Java: Oracle GraalVM Native Image’s Startup, Memory, and Throughput Gains
DataFunSummit
DataFunSummit
Jun 14, 2023 · Artificial Intelligence

DataFun Summit 2023: Large Language Models and AIGC Conference

DataFun will host the DataFun Summit 2023 on June 17‑18, featuring three chairs and eight presenters who will discuss core topics such as large language model research, multimodal generation, reinforcement learning, tool learning, distributed training, and industry applications, with free registration via QR code.

AI ConferenceAIGCMultimodal
0 likes · 42 min read
DataFun Summit 2023: Large Language Models and AIGC Conference
WeChat Backend Team
WeChat Backend Team
Jun 13, 2023 · Artificial Intelligence

Boosting Vertical Federated Learning: Optimizing Paillier Encryption & Model Stability

This article examines the challenges of data privacy in big‑data environments and presents a comprehensive approach to vertical federated learning, detailing framework optimizations, Paillier homomorphic encryption enhancements, PSI‑based feature selection, and adversarial learning techniques to improve model stability and deployment on a unified ML platform.

Model StabilityPaillier encryptionPrivacy Computing
0 likes · 19 min read
Boosting Vertical Federated Learning: Optimizing Paillier Encryption & Model Stability
Python Programming Learning Circle
Python Programming Learning Circle
Jun 12, 2023 · Artificial Intelligence

10 Common Loss Functions and Their Python Implementations

This article explains ten widely used loss functions for regression and classification tasks, describes their mathematical definitions, compares their purposes, and provides complete Python code examples for each, helping readers understand how to select and implement appropriate loss metrics in machine‑learning models.

AIClassificationLoss Functions
0 likes · 10 min read
10 Common Loss Functions and Their Python Implementations
Architect
Architect
Jun 10, 2023 · Artificial Intelligence

An Overview of Twitter’s Open‑Source Recommendation System Architecture

Twitter’s recently open‑sourced recommendation system is dissected, covering its overall architecture, graph‑based data and feature engineering, recall pipelines (in‑network and out‑of‑network), coarse and fine ranking models, mixing and re‑ranking stages, as well as the supporting infrastructure and code examples.

Ranking ModelsTwittergraph embedding
0 likes · 16 min read
An Overview of Twitter’s Open‑Source Recommendation System Architecture
Bilibili Tech
Bilibili Tech
Jun 9, 2023 · Artificial Intelligence

Implementing Face Blocking Danmaku Using Machine Learning in Browser

Liu Jun explains how Bilibili’s engineers replaced traditional pre‑processed SVG masks with a real‑time, browser‑based machine‑learning pipeline—using MediaPipe SelfieSegmentation, OffscreenCanvas, and Web Workers—to extract human contours and block faces in danmaku, achieving roughly 5 % CPU load on a 2020 M1 MacBook.

Face DetectionMediaPipeWeb Workers
0 likes · 9 min read
Implementing Face Blocking Danmaku Using Machine Learning in Browser
DataFunTalk
DataFunTalk
Jun 4, 2023 · Artificial Intelligence

Co‑training Disentangled Domain Adaptation Network for Leveraging Popularity Bias in Recommender Systems

This presentation introduces a decoupled domain‑adaptation network that separates popularity and attribute representations to mitigate popularity bias in recommender systems, describing the problem, existing IPS and causal‑inference solutions, the CD2AN architecture, experimental results, and practical Q&A.

AIDomain Adaptationmachine learning
0 likes · 13 min read
Co‑training Disentangled Domain Adaptation Network for Leveraging Popularity Bias in Recommender Systems
Architecture Digest
Architecture Digest
Jun 2, 2023 · Artificial Intelligence

Overview of Twitter's Open‑Source Recommendation Algorithm

Twitter has open‑sourced its core recommendation algorithm, detailing its candidate sources, in‑network and out‑of‑network ranking models, graph‑based and embedding methods, and a comprehensive list of components that power the home timeline, with links to the GitHub repositories and engineering blog.

Artificial IntelligenceTwittermachine learning
0 likes · 10 min read
Overview of Twitter's Open‑Source Recommendation Algorithm
DataFunTalk
DataFunTalk
Jun 1, 2023 · Artificial Intelligence

Counterfactual Causal Inference for Credit‑Limit Modeling (Mono‑CFR)

This article presents a comprehensive overview of causal inference paradigms, the evolution of uplift and representation‑learning frameworks, and introduces the Mono‑CFR counterfactual credit‑limit model that estimates treatment effects for continuous credit limits using observational data while addressing confounding factors.

AICausal Inferencecounterfactual learning
0 likes · 14 min read
Counterfactual Causal Inference for Credit‑Limit Modeling (Mono‑CFR)
58 Tech
58 Tech
May 26, 2023 · Artificial Intelligence

A2M Summit: AI & Machine Learning – Recommendation Algorithms in 58.com’s Industrial Transformation

The A2M Summit announcement details a 2023 AI and machine learning conference where senior algorithm architect Liu Lixi presents his talk on practical recommendation system techniques for sparse data, low‑frequency scenarios, and ad‑creative optimization within 58.com’s industry‑wide digital transformation.

58.comAIIndustrial Transformation
0 likes · 5 min read
A2M Summit: AI & Machine Learning – Recommendation Algorithms in 58.com’s Industrial Transformation
Top Architect
Top Architect
May 25, 2023 · Artificial Intelligence

A Brief Overview of Graph Neural Networks: GCN, GraphSAGE, GAT, GAE and DiffPool

This article provides an introductory overview of graph neural networks, explaining their motivation, basic concepts, and detailing classic models such as GCN, GraphSAGE, GAT, Graph Auto‑Encoder, and DiffPool, along with their advantages, limitations, and experimental results on various benchmark datasets.

DiffPoolGATGCN
0 likes · 20 min read
A Brief Overview of Graph Neural Networks: GCN, GraphSAGE, GAT, GAE and DiffPool
Smart Era Software Development
Smart Era Software Development
May 25, 2023 · Artificial Intelligence

The New Paradigm of Large Models: How AI is Reshaping Industries

The rapid rise of large AI models is creating a new paradigm that transforms how information, knowledge, and action intertwine across technology, industry, and society, driving unprecedented shifts in digital platforms, product development, and global economic structures.

AI paradigm shiftdigital economyindustry transformation
0 likes · 86 min read
The New Paradigm of Large Models: How AI is Reshaping Industries
21CTO
21CTO
May 22, 2023 · Artificial Intelligence

Google Brings Codey-Powered AI Coding to Colab – Free Copilot Alternative

Google announced that Colab will soon offer AI-driven code generation, completion, and chat assistance powered by the Codey model, providing a free, multi‑language development tool that rivals GitHub Copilot for Python and machine‑learning workflows.

AI code generationCodeyGitHub Copilot
0 likes · 4 min read
Google Brings Codey-Powered AI Coding to Colab – Free Copilot Alternative
Programmer DD
Programmer DD
May 22, 2023 · Artificial Intelligence

Why Apple Banned ChatGPT for Employees and What It Means for Siri’s AI Future

Apple has prohibited its staff from using ChatGPT and other generative AI tools over data‑leakage concerns, mirroring similar policies at Amazon, Microsoft and JPMorgan, while simultaneously developing its own large‑model‑powered Siri features under the codename Bobcat.

AppleArtificial IntelligenceChatGPT
0 likes · 7 min read
Why Apple Banned ChatGPT for Employees and What It Means for Siri’s AI Future
Top Architect
Top Architect
May 8, 2023 · Artificial Intelligence

Understanding Stable Diffusion: Architecture, Training, and Practical Applications

This article provides a comprehensive overview of Stable Diffusion, covering its latent diffusion architecture, training data and procedures, model components such as autoencoder, CLIP text encoder and UNet, as well as practical usage examples including text‑to‑image generation, image‑to‑image, inpainting, and advanced extensions like ControlNet and SD‑2.x.

AI image generationDiffusion ModelsStable Diffusion
0 likes · 52 min read
Understanding Stable Diffusion: Architecture, Training, and Practical Applications
DataFunTalk
DataFunTalk
May 3, 2023 · Artificial Intelligence

Causal Inference for Incentive and Supply‑Demand Optimization in Tencent Weishi

This article presents a comprehensive overview of applying causal inference techniques to Tencent Weishi's cash incentive and video supply‑demand optimization, detailing business modeling, algorithmic frameworks, treatment representations, constrained multivariate causal models, experimental evaluations, and practical deployment insights.

Causal Inferenceincentive optimizationmachine learning
0 likes · 32 min read
Causal Inference for Incentive and Supply‑Demand Optimization in Tencent Weishi
DaTaobao Tech
DaTaobao Tech
Apr 28, 2023 · Artificial Intelligence

Multi-Scenario Recommendation Model

The paper introduces SASS, a scenario-adaptive self-supervised recommendation model that uses contrastive pre-training and multi-layer gating to expand global samples and transfer scene-aware parameters, enabling a single model to deliver personalized recommendations across diverse Taobao ‘SuoSuo’ scenarios while mitigating data sparsity and cross-domain challenges.

AIPersonalized Recommendationdata modeling
0 likes · 23 min read
Multi-Scenario Recommendation Model
21CTO
21CTO
Apr 27, 2023 · Artificial Intelligence

John Carmack’s Bold Quest: Building AGI from Game Engines to Rocket Science

In an in‑depth interview, legendary game developer and VR pioneer John Carmack explains why he left Meta to pursue artificial general intelligence through his startup Keen Technologies, outlining his independent research approach, predictions for AGI breakthroughs, and the potential economic impact of truly general AI.

AGIArtificial IntelligenceJohn Carmack
0 likes · 22 min read
John Carmack’s Bold Quest: Building AGI from Game Engines to Rocket Science
DataFunTalk
DataFunTalk
Apr 26, 2023 · Artificial Intelligence

Serializing Advertising Placement with User Algorithms at Alibaba Health

Alibaba Health’s user algorithm leverages multi‑channel serialized ad placement, using vector‑based three‑tower models, knowledge distillation, and ROI‑oriented optimizations to sequence user touchpoints, improve conversion rates, and enhance model accuracy across diverse marketing channels.

AdvertisingROIUser Segmentation
0 likes · 15 min read
Serializing Advertising Placement with User Algorithms at Alibaba Health
DevOps
DevOps
Apr 25, 2023 · Artificial Intelligence

The Bitter Lesson: Why Brute‑Force Computation Outperforms Hand‑Crafted Knowledge in AI

Richard Sutton’s “The Bitter Lesson” argues that over the past seven decades the most powerful driver of AI progress has been general‑purpose compute and large‑scale search, which consistently surpasses methods that rely on human‑engineered knowledge across domains such as chess, Go, speech recognition, and computer vision.

AIbrute-forcecomputational scaling
0 likes · 7 min read
The Bitter Lesson: Why Brute‑Force Computation Outperforms Hand‑Crafted Knowledge in AI
DataFunSummit
DataFunSummit
Apr 22, 2023 · Artificial Intelligence

Applying Causal Inference to Limited‑Resource Decision‑Making

This article explains the fundamentals of causal inference, illustrates its distinction from correlation modeling, and demonstrates how causal techniques can be applied to limited‑resource decision problems such as knapsack optimization, ride‑hailing subsidies, and flight pricing, while also covering experimental design, popular models, evaluation metrics, and open challenges.

Causal InferenceDecision OptimizationExperimental Design
0 likes · 15 min read
Applying Causal Inference to Limited‑Resource Decision‑Making
Alimama Tech
Alimama Tech
Apr 19, 2023 · Artificial Intelligence

Potential Generalized Second Price (PGSP) Auction for Augmented Advertising

This paper proposes a two‑stage Potential Generalized Second Price auction for augmented ads, ranking guide ads by expected welfare from their linked second‑step ads, shifting billing to the second click to eliminate free‑riding, and demonstrates via offline and online experiments on Taobao that it boosts click‑through, revenue, and GMV while lowering CPC.

Online Advertisinge-commercemachine learning
0 likes · 16 min read
Potential Generalized Second Price (PGSP) Auction for Augmented Advertising
DataFunSummit
DataFunSummit
Apr 13, 2023 · Databases

AI-Driven and Cost-Based Index Recommendation for Slow Queries at Meituan

This article describes Meituan's collaboration with East China Normal University to improve slow‑query performance by combining traditional cost‑based index recommendation with a novel AI‑plus‑data‑driven approach, detailing the overall architecture, modeling process, experimental results, and future plans for large‑model integration.

AIIndex RecommendationMeituan
0 likes · 15 min read
AI-Driven and Cost-Based Index Recommendation for Slow Queries at Meituan
Tencent Cloud Developer
Tencent Cloud Developer
Apr 10, 2023 · Artificial Intelligence

How Computers Generate Realistic Images: An In‑Depth Guide to AI Image Generation, Diffusion Models, ControlNet, LoRA and More

This guide explains how AI creates photorealistic images, tracing the shift from VAEs and GANs to diffusion models, detailing latent diffusion, ControlNet conditioning, CLIP text‑image alignment, and lightweight fine‑tuning methods like DreamBooth and LoRA, plus practical tips for higher‑resolution results.

AI image generationControlNetDiffusion Models
0 likes · 22 min read
How Computers Generate Realistic Images: An In‑Depth Guide to AI Image Generation, Diffusion Models, ControlNet, LoRA and More
Meituan Technology Team
Meituan Technology Team
Apr 6, 2023 · Databases

AI-Driven Index Recommendation for Slow Queries at Meituan

This article details a joint research effort between Meituan and East China Normal University that combines cost‑based methods with AI‑driven, data‑centric models to automatically generate and evaluate missing indexes for billions of daily slow queries, improving recommendation accuracy and query performance.

AICost ModelIndex Recommendation
0 likes · 16 min read
AI-Driven Index Recommendation for Slow Queries at Meituan
DataFunTalk
DataFunTalk
Apr 5, 2023 · Artificial Intelligence

Advances in Causal Representation Learning: From i.i.d. to Non‑Stationary Settings

This article reviews recent developments in causal representation learning, explaining why causal reasoning is essential, describing methods for i.i.d. data, time‑series, and multi‑distribution scenarios, and illustrating applications such as domain adaptation, video analysis, and financial data with numerous examples and visualizations.

Causal InferenceDomain Adaptationcausal discovery
0 likes · 22 min read
Advances in Causal Representation Learning: From i.i.d. to Non‑Stationary Settings
Programmer DD
Programmer DD
Apr 4, 2023 · Artificial Intelligence

Inside Twitter’s Open‑Source Recommendation Engine: How It Ranks Your Timeline

Twitter has finally open‑sourced most of its recommendation algorithm, revealing a three‑stage pipeline that gathers top tweets, ranks them with machine‑learning models, and filters out unwanted content, while also exposing the massive graph data and ranking signals that power the For You timeline.

Twittermachine learningrecommendation algorithm
0 likes · 11 min read
Inside Twitter’s Open‑Source Recommendation Engine: How It Ranks Your Timeline
HelloTech
HelloTech
Apr 3, 2023 · Artificial Intelligence

Integrating Machine Learning with Elasticsearch for Enhanced Ranking Capabilities

At the 2023 Elastic China Developer Conference in Shenzhen, Peng Cheng of Hello Technology will demonstrate how migrating online machine‑learning predictions into Elasticsearch can exploit its distributed architecture to rank thousands of models, expand model types and computational depth, and unlock new growth opportunities for business applications, underscoring the event’s status as China’s premier Elasticsearch open‑source technology forum.

ElasticsearchTechnical Conferencemachine learning
0 likes · 2 min read
Integrating Machine Learning with Elasticsearch for Enhanced Ranking Capabilities
21CTO
21CTO
Apr 1, 2023 · Artificial Intelligence

Inside Twitter’s Open‑Source Recommendation Engine: How Tweets Are Ranked

Twitter has released the source code of its recommendation algorithm, revealing a three‑stage pipeline that collects, ranks with machine‑learning models, and filters tweets to balance content from followed and unfollowed accounts while optimizing for user engagement.

Artificial IntelligenceTwittermachine learning
0 likes · 5 min read
Inside Twitter’s Open‑Source Recommendation Engine: How Tweets Are Ranked
Java Architecture Diary
Java Architecture Diary
Apr 1, 2023 · Artificial Intelligence

Inside Twitter’s Open‑Source Recommendation Engine: Architecture & Key Components

This article examines the open‑source Twitter recommendation algorithm released by Elon Musk, detailing its main services, machine‑learning models, data sources, programming languages, and the GitHub repositories that host the core components such as SimClusters, TwHIN, rankers, and the Rust‑based navi framework.

Twitterbackend architecturemachine learning
0 likes · 5 min read
Inside Twitter’s Open‑Source Recommendation Engine: Architecture & Key Components
HomeTech
HomeTech
Mar 31, 2023 · Artificial Intelligence

Digital Transformation of Used‑Car Buying: Integrated Data, AI Valuation, and VR Visualization

The article describes how a comprehensive digital platform combines structured, semi‑structured, and panoramic data with machine‑learning valuation models, natural‑language processing, and VR technology to make used‑car condition information transparent, improve estimation accuracy, and enhance user decision‑making in the Chinese second‑hand car market.

AI valuationData IntegrationVR Visualization
0 likes · 15 min read
Digital Transformation of Used‑Car Buying: Integrated Data, AI Valuation, and VR Visualization
Python Programming Learning Circle
Python Programming Learning Circle
Mar 27, 2023 · Artificial Intelligence

Top 10 Machine Learning Algorithms: Concepts, Uses, and Key Characteristics

This article introduces the No‑Free‑Lunch principle in machine learning and provides concise explanations of ten fundamental supervised‑learning algorithms—including linear regression, logistic regression, LDA, decision trees, Naïve Bayes, K‑Nearest Neighbors, LVQ, SVM, random forest, and boosting—highlighting their mathematical basis, typical applications, advantages, and limitations.

Artificial IntelligenceData Sciencemachine learning
0 likes · 12 min read
Top 10 Machine Learning Algorithms: Concepts, Uses, and Key Characteristics
DaTaobao Tech
DaTaobao Tech
Mar 24, 2023 · Artificial Intelligence

Leveraging Popularity Bias with Decoupled Unbiased Recall Models

In a March 27 livestream, Alibaba senior algorithm engineer Chen Zhihong will explain how popularity bias affects recommendation pipelines, review existing mitigation techniques, and introduce a decoupled domain‑adaptive unbiased dual‑tower recall model that leverages bias while preserving recommendation fairness.

Unbiased Recallmachine learningpopularity bias
0 likes · 2 min read
Leveraging Popularity Bias with Decoupled Unbiased Recall Models
DataFunSummit
DataFunSummit
Mar 22, 2023 · Artificial Intelligence

Sales Forecasting in Alibaba Health's Pharmaceutical E‑commerce: Business Background, Algorithm Solutions, and Scenario Exploration

The article details a comprehensive presentation on Alibaba Health's pharmaceutical e‑commerce sales forecasting, covering supply‑chain challenges, the evolution of time‑series prediction methods, a full data‑to‑model pipeline, change‑point detection, handling imbalanced data, multi‑model fusion, and specialized seasonal and long‑sequence forecasting techniques.

Alibaba HealthSales ForecastingSupply Chain
0 likes · 16 min read
Sales Forecasting in Alibaba Health's Pharmaceutical E‑commerce: Business Background, Algorithm Solutions, and Scenario Exploration