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1966 articles · Page 15 of 20
AntTech
AntTech
Dec 4, 2019 · Artificial Intelligence

Ant Financial’s Online Learning System Built on Ray: Architecture, Challenges, and Future Plans

The interview details how Ant Financial transitioned from offline to online machine learning by adopting the Ray distributed engine, describing their open architecture, fusion computing approach, technical advantages, encountered pitfalls, and plans to open‑source the system for broader AI and big‑data use.

AIAnt FinancialDistributed Computing
0 likes · 15 min read
Ant Financial’s Online Learning System Built on Ray: Architecture, Challenges, and Future Plans
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 MitigationEmbeddingdata preprocessing
0 likes · 32 min read
Feature Engineering Practices for Short‑Video Recommendation Systems
58 Tech
58 Tech
Nov 29, 2019 · Artificial Intelligence

Ranking Strategy Optimization Practices for Commercial Traffic at 58.com

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

Real-time Data Pipelinefeature engineeringmachine learning
0 likes · 17 min read
Ranking Strategy Optimization Practices for Commercial Traffic at 58.com
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.

Map SearchNLPcity detection
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 treefeature extractionlayout tree
0 likes · 8 min read
Web Data Mining and Page Analysis Techniques for Search Engines
Amap Tech
Amap Tech
Nov 21, 2019 · Artificial Intelligence

Advances in Geographic Text Processing for Map Search: Query Analysis, Error Correction, Rewriting, and Omission

Recent advances in map‑search text processing replace rule‑based pipelines with machine‑learning and deep‑learning models for query analysis, error correction, rewriting, and omission, using phonetic and spatial entity correction, vector‑based similarity, and CRF sequence labeling within a three‑stage architecture of analysis, recall, and ranking to deliver more precise POI results.

Error CorrectionMap SearchNLP
0 likes · 17 min read
Advances in Geographic Text Processing for Map Search: Query Analysis, Error Correction, Rewriting, and Omission
Alibaba Terminal Technology
Alibaba Terminal Technology
Nov 21, 2019 · Frontend Development

How AI Is Revolutionizing Front‑End Development: From Design Drafts to Code

This article explores the rise of front‑end intelligence, analyzing background trends, competitive solutions, problem decomposition, and technical approaches for automatically converting design drafts into HTML, CSS, and JavaScript, while discussing challenges, model accuracy, data quality, and future roadmap for the D2C (Design‑to‑Code) system.

AICode Generationautomation
0 likes · 20 min read
How AI Is Revolutionizing Front‑End Development: From Design Drafts to Code
Hulu Beijing
Hulu Beijing
Nov 15, 2019 · Artificial Intelligence

How Content-Based Video Relevance Prediction Advances Personalized Streaming

The CBVRP (Content-Based Video Relevance Prediction) challenge, co‑hosted by Hulu and ACM MM 2019, showcased the shift from user‑based collaborative filtering to content‑driven recommendation, highlighted winning teams and their papers, and underscored the ongoing research importance of cold‑start video recommendation for streaming platforms.

Streamingcold startcontent-based recommendation
0 likes · 15 min read
How Content-Based Video Relevance Prediction Advances Personalized Streaming
58 Tech
58 Tech
Nov 15, 2019 · Artificial Intelligence

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

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

AB TestingKnowledge GraphPersonalized Recommendation
0 likes · 18 min read
From Zero to One: Building a Personalized Recommendation System for 58.com Recruitment Platform
DataFunTalk
DataFunTalk
Nov 15, 2019 · Artificial Intelligence

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

This article details how 58.com constructed a large‑scale personalized recommendation platform for its recruitment business, covering business background, user intent modeling, knowledge‑graph and NER techniques, user profiling, multi‑stage recall strategies, ranking model pipelines, serving infrastructure, AB testing, and future research directions.

CTRCVRKnowledge Graph
0 likes · 18 min read
From Zero to One: Building 58.com Recruitment Personalized Recommendation System
Alibaba Cloud Developer
Alibaba Cloud Developer
Nov 15, 2019 · Artificial Intelligence

Boosting Online Shopping with AI-Powered 3D Scene Merchandising

This article explores how Alibaba’s 3D scene‑based recommendation system combines computer‑vision, deep‑learning and data‑driven matching algorithms to create immersive, size‑accurate product visualizations that enhance user experience and drive higher click‑through rates in e‑commerce.

3D Visualizationdeep learninge-commerce
0 likes · 12 min read
Boosting Online Shopping with AI-Powered 3D Scene Merchandising
360 Tech Engineering
360 Tech Engineering
Nov 13, 2019 · Artificial Intelligence

Text Anti‑Spam Techniques and TextCNN Model for Real‑Time Spam Detection on the Huajiao Platform

This article introduces the Huajiao platform's text anti‑spam architecture, analyzes spam categories and challenges, compares rule‑based and machine‑learning approaches, details traditional NLP methods and the TextCNN deep‑learning model, provides its TensorFlow implementation, and describes the online deployment workflow.

CNNNLPTensorFlow
0 likes · 14 min read
Text Anti‑Spam Techniques and TextCNN Model for Real‑Time Spam Detection on the Huajiao Platform
Xianyu Technology
Xianyu Technology
Nov 13, 2019 · Artificial Intelligence

Real-time Feature Engineering for Recommendation Systems via Edge Computing

The article proposes moving real‑time feature engineering for recommendation systems from cloud to edge devices, enabling second‑level updates of user behavior features such as exposure, scroll speed, and clicks, which reduces latency, improves model freshness and recommendation accuracy through edge‑cloud collaboration.

edge computingmachine learningrecommendation
0 likes · 6 min read
Real-time Feature Engineering for Recommendation Systems via Edge Computing
vivo Internet Technology
vivo Internet Technology
Nov 12, 2019 · Artificial Intelligence

Elasticsearch Retrieval Optimization in Gitee: Interview with Chen Xin

In an interview, Gitee’s chief architect Chen Xin explains why Elasticsearch was chosen for code search, outlines how combining search with NLP can both aid semantic understanding and enrich repository queries, and shares his views on the platform’s fast‑evolving ecosystem and upcoming community meetup.

ElasticsearchGiteeInformation Retrieval
0 likes · 4 min read
Elasticsearch Retrieval Optimization in Gitee: Interview with Chen Xin
58 Tech
58 Tech
Nov 11, 2019 · Artificial Intelligence

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

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

LightGBMXGBoostcar price estimation
0 likes · 11 min read
Design and Implementation of the 58 Car Price Estimation System Using Machine Learning
Alibaba Cloud Developer
Alibaba Cloud Developer
Nov 8, 2019 · Artificial Intelligence

How AI is Transforming Software Testing: From Manual Checks to Fully Automated Intelligence

This article explores the evolution of software testing, the challenges posed by rapid internet-driven development, a six‑level model of AI‑augmented testing, practical application scenarios such as unit, API and UI testing, and a survey of leading AI‑powered testing tools shaping the future of quality assurance.

AI testingmachine learningquality assurance
0 likes · 15 min read
How AI is Transforming Software Testing: From Manual Checks to Fully Automated Intelligence
360 Zhihui Cloud Developer
360 Zhihui Cloud Developer
Nov 5, 2019 · Operations

How 360 Scaled AIOps: From Data to Self‑Healing Operations

At the 360 Internet Technology Training Camp, experts detailed how AI-driven AIOps can transform large‑scale operations, covering data collection, model‑based anomaly detection, alert correlation, self‑healing workflows, and visual dashboards, and presented a practical end‑to‑end framework that other companies can adopt quickly.

AIOpsOperationsSelf-Healing
0 likes · 15 min read
How 360 Scaled AIOps: From Data to Self‑Healing Operations
DataFunTalk
DataFunTalk
Nov 4, 2019 · Artificial Intelligence

Standardizing Model Training and Feature Processing in Recommendation Systems

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

CTR predictionStandardizationfeature engineering
0 likes · 14 min read
Standardizing Model Training and Feature Processing in Recommendation Systems
Python Programming Learning Circle
Python Programming Learning Circle
Nov 3, 2019 · Artificial Intelligence

Build Machine Learning Apps in Minutes with Streamlit: A Python‑Only Guide

This article explains how machine‑learning engineers can create fully functional, interactive apps using only Python and the open‑source Streamlit framework, covering its core principles, widget handling, caching, GPU support, deployment workflow, and real‑world examples with code snippets and diagrams.

App DevelopmentData VisualizationPython
0 likes · 9 min read
Build Machine Learning Apps in Minutes with Streamlit: A Python‑Only Guide
58 Tech
58 Tech
Oct 28, 2019 · Artificial Intelligence

Ranking Strategy Optimization Practice in 58 Commercial Traffic

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

Real-time Data Pipelinee-commercefeature engineering
0 likes · 16 min read
Ranking Strategy Optimization Practice in 58 Commercial Traffic
Amap Tech
Amap Tech
Oct 23, 2019 · Artificial Intelligence

Scene‑aware and Fine‑grained Positioning Technology: Insights from Gaode Maps

Gaode’s scene‑aware positioning platform, serving billions of daily requests across 300,000 apps, combines GPS, Wi‑Fi/base‑station fingerprinting, inertial navigation, map‑matching and AI‑driven models to deliver sub‑10‑meter accuracy indoors and in vehicles, while leveraging massive data loops, hierarchical ranking, feature compression, and exploring 5G, VSLAM and ultra‑wideband for future fine‑grained localization.

AIMap Serviceslocation
0 likes · 16 min read
Scene‑aware and Fine‑grained Positioning Technology: Insights from Gaode Maps
DataFunTalk
DataFunTalk
Oct 17, 2019 · Artificial Intelligence

iQIYI Effect Advertising: Architecture, Click & Conversion Rate Estimation, and Intelligent Bidding

This article presents iQIYI's effect advertising system, detailing its dual‑engine resource slots, oCPX billing model, algorithmic challenges of high‑dimensional sparse conversion data, the personalized recommendation pipeline, feature engineering across real‑time, short‑term and long‑term signals, and the intelligent bidding mechanism that balances cost control with traffic quality.

AdvertisingConversion Optimizationclick-through rate
0 likes · 9 min read
iQIYI Effect Advertising: Architecture, Click & Conversion Rate Estimation, and Intelligent Bidding
58 Tech
58 Tech
Oct 12, 2019 · Artificial Intelligence

Recruitment Recommendation System: Ranking Framework, Model Evolution, and Feature Engineering

This article details 58.com’s recruitment recommendation platform, describing its personalized matching challenges, typical recommendation scenarios, a three‑stage ranking framework, optimization goals, the evolution from rule‑based methods to logistic regression, factorization machines, XGBoost, and deep learning models, extensive feature engineering practices, and future research directions.

AIdeep learningfeature engineering
0 likes · 16 min read
Recruitment Recommendation System: Ranking Framework, Model Evolution, and Feature Engineering
Architects' Tech Alliance
Architects' Tech Alliance
Oct 11, 2019 · Cloud Computing

Understanding FPGA: Architecture, Advantages, and Microsoft’s Data‑Center Deployments

This article explains what FPGA (Field‑Programmable Gate Array) is, why it offers lower latency and higher energy efficiency than CPUs or GPUs for both compute‑intensive and communication‑intensive workloads, and details Microsoft’s three‑generation FPGA deployment strategy in its data‑center and cloud infrastructure.

FPGANetworkingdata center
0 likes · 20 min read
Understanding FPGA: Architecture, Advantages, and Microsoft’s Data‑Center Deployments
Meituan Technology Team
Meituan Technology Team
Oct 10, 2019 · Artificial Intelligence

Iterative Development of Delivery Time Estimation Models: Tree Model, Vector Retrieval, and End‑to‑End Deep Learning

The paper chronicles Meituan’s three‑stage evolution of delivery‑time estimation—from a hierarchical address tree with local linear regression, through a vector‑retrieval system that boosts recall, to a lightweight end‑to‑end deep‑learning model that meets sub‑5 ms latency while delivering progressively lower error and full coverage.

Performance OptimizationVector Retrievaldeep learning
0 likes · 21 min read
Iterative Development of Delivery Time Estimation Models: Tree Model, Vector Retrieval, and End‑to‑End Deep Learning
Alibaba Cloud Developer
Alibaba Cloud Developer
Oct 10, 2019 · Artificial Intelligence

Boosting Spring Festival Activity: Alibaba’s Full‑Link Intelligent Delivery Framework

This article explains how Alibaba’s Hand‑Taobao platform uses a full‑link intelligent delivery framework—combining user intent recognition, rights recommendation, and advanced machine‑learning models such as XFTRL and Thompson Sampling—to predict activity drops during the Spring Festival and deliver personalized interventions that significantly improve DAU, click‑through, and redemption rates.

A/B testingUser Intente-commerce
0 likes · 12 min read
Boosting Spring Festival Activity: Alibaba’s Full‑Link Intelligent Delivery Framework
Architects Research Society
Architects Research Society
Oct 7, 2019 · Artificial Intelligence

Comparison of Deep Learning Software Frameworks and Libraries

The article introduces deep learning as a machine‑learning subfield aimed at achieving artificial intelligence, explains its advantages in pattern recognition, and presents a visual comparison of prominent deep‑learning software frameworks, related tools, and additional resources while also including promotional information for a WeChat community.

AI frameworksmachine learningneural networks
0 likes · 3 min read
Comparison of Deep Learning Software Frameworks and Libraries
21CTO
21CTO
Oct 6, 2019 · Artificial Intelligence

How Toutiao’s AI Recommendation Engine Works: From Content Analysis to Real‑Time Ranking

This article explains the architecture and principles of Toutiao’s recommendation system, covering its three‑dimensional model of content, user and environment features, content analysis techniques, user tagging, real‑time training pipelines, evaluation methods, and content safety measures that together drive personalized feeds.

Content AnalysisReal-time TrainingUser Modeling
0 likes · 18 min read
How Toutiao’s AI Recommendation Engine Works: From Content Analysis to Real‑Time Ranking
MaGe Linux Operations
MaGe Linux Operations
Sep 27, 2019 · Artificial Intelligence

Top 10 Python Libraries Every AI Developer Should Master

This article introduces ten essential Python libraries—TensorFlow, Scikit‑Learn, NumPy, Keras, PyTorch, LightGBM, Eli5, SciPy, Theano, and Pandas—detailing their features, typical use cases, and adoption in machine‑learning and data‑science projects, while highlighting each library's performance advantages, community support, and integration capabilities to help developers choose the right tool for their AI workflows.

KerasNumPyPyTorch
0 likes · 15 min read
Top 10 Python Libraries Every AI Developer Should Master
ITPUB
ITPUB
Sep 27, 2019 · Artificial Intelligence

How Didi and Ant Financial Co‑Built SQLFlow to Bring AI to Data Analysts

The article describes how Didi's data science team partnered with Ant Financial to open‑source SQLFlow, a tool that translates SQL into Python for AI model training and inference, enabling analysts to use familiar SQL to run deep‑learning, XGBoost, and clustering models across diverse business scenarios.

AIData ScienceSQL
0 likes · 9 min read
How Didi and Ant Financial Co‑Built SQLFlow to Bring AI to Data Analysts
ITPUB
ITPUB
Sep 25, 2019 · Artificial Intelligence

From Physics to Kaggle Grandmaster: A Data Scientist’s Journey and Advice

Physicist‑turned‑Kaggle Grandmaster Bojan Tunguz shares his journey from academia to industry, the challenges of becoming a data‑science competitor, his workflow, favorite tools, and practical advice for newcomers seeking to excel in machine‑learning competitions.

Artificial IntelligenceData ScienceKaggle
0 likes · 10 min read
From Physics to Kaggle Grandmaster: A Data Scientist’s Journey and Advice
DataFunTalk
DataFunTalk
Sep 25, 2019 · Artificial Intelligence

Practical Exploration of OCPC Advertising Algorithm at Phoenix New Media

This article presents a comprehensive overview of the OCPC (Optimized Cost Per Click) advertising algorithm deployed by Phoenix New Media, detailing its background, problem definition, two‑price mechanism, smart bidding, CVR estimation techniques, online learning architecture, challenges such as data sparsity and conversion delay, and future research directions.

CVR estimationOCPCadvertising algorithm
0 likes · 15 min read
Practical Exploration of OCPC Advertising Algorithm at Phoenix New Media
DataFunTalk
DataFunTalk
Sep 23, 2019 · Artificial Intelligence

Understanding UC International Feed Recommendation: Goal Determination, Multi‑Objective Estimation, and Mixed Ranking

This article explains how UC international feed recommendation tackles goal definition, multi‑objective point estimation using models such as ESMM, DBMTL and MMoE, mixed‑ranking optimization, and cold‑start challenges by leveraging content understanding and feature generalization to improve user satisfaction.

AIcold-startmachine learning
0 likes · 12 min read
Understanding UC International Feed Recommendation: Goal Determination, Multi‑Objective Estimation, and Mixed Ranking
DataFunTalk
DataFunTalk
Sep 20, 2019 · Artificial Intelligence

Diversity as a Means, Not an End, in Recommendation Systems

The article argues that diversity should be treated as a tool rather than a final objective in recommendation systems, explains why it is hard to quantify, discusses appropriate metrics such as user feedback and engagement, and presents practical strategies—including expert rules, richer recall pipelines, and list‑wise modeling—to improve diversity while optimizing true business goals.

diversitylistwisemachine learning
0 likes · 7 min read
Diversity as a Means, Not an End, in Recommendation Systems
Xianyu Technology
Xianyu Technology
Sep 17, 2019 · Frontend Development

Large-Scale UI Sample Generation for Alibaba 99 Promotion Module Recognition

The article describes a pipeline that automatically extracts a JSON‑like DSL representation of Alibaba’s 99‑promotion UI from rendered pages, cleanses CSS, converts transforms, renders the DSL to images, and combines it with dynamic ViewModel data to generate tens of thousands of high‑quality samples per module, raising recognition accuracy above 98%.

DSLFront-endPuppeteer
0 likes · 8 min read
Large-Scale UI Sample Generation for Alibaba 99 Promotion Module Recognition
DataFunTalk
DataFunTalk
Sep 17, 2019 · Artificial Intelligence

Machine Learning for Personalized Education Paths – Case Study and Reflections

This lecture explores how machine learning can generate individualized learning pathways for students by building knowledge dependency graphs, defining optimization goals, and leveraging historical data to rank candidate routes, while reflecting on data, model, business, and demand challenges in AI-driven education.

AIKnowledge Graphbig data
0 likes · 10 min read
Machine Learning for Personalized Education Paths – Case Study and Reflections
360 Tech Engineering
360 Tech Engineering
Sep 16, 2019 · Artificial Intelligence

Backpropagation Algorithm for Fully Connected Neural Networks with Python Implementation

This article explains the backpropagation training algorithm for fully connected artificial neural networks, detailing its gradient‑descent basis, mathematical derivation, matrix formulation, and provides a complete Python implementation with mini‑batch stochastic gradient descent, momentum, learning‑rate decay, and experimental results.

BackpropagationMini-BatchNeural Network
0 likes · 14 min read
Backpropagation Algorithm for Fully Connected Neural Networks with Python Implementation
DataFunTalk
DataFunTalk
Sep 16, 2019 · Artificial Intelligence

Evolution of Weibo Advertising Strategy Engineering Architecture

This article presents a comprehensive overview of the evolution of Weibo's advertising strategy engineering architecture, detailing the system's growth from early banner ads to a sophisticated, multi‑layered online advertising platform that integrates algorithmic models, A/B experimentation, real‑time data pipelines, and precision targeting to support scalable, high‑performance ad delivery.

A/B testingAdvertisingOnline Advertising
0 likes · 19 min read
Evolution of Weibo Advertising Strategy Engineering Architecture
DataFunTalk
DataFunTalk
Sep 12, 2019 · Artificial Intelligence

Exploring Personalized Recommendation at Kuaikan Comics: Business, Algorithms, and System Architecture

This article details Kuaikan Comics' personalized recommendation pipeline, covering business context, diverse content formats, technical challenges, content‑based and collaborative‑filtering methods, ranking models, system architecture, A/B testing, and future directions for improving recommendation quality.

A/B testingCTR predictioncollaborative filtering
0 likes · 14 min read
Exploring Personalized Recommendation at Kuaikan Comics: Business, Algorithms, and System Architecture
DataFunTalk
DataFunTalk
Sep 10, 2019 · Artificial Intelligence

Computational Advertising: Overview and Key Techniques from the Second Edition

The article introduces the second edition of "Computational Advertising", highlights its practical coverage of bidding algorithms, eCPM estimation, query expansion methods, and ad placement optimization, while also noting its industry impact, author credentials, and a limited‑time book giveaway.

Ad TechQuery Expansionbidding algorithms
0 likes · 10 min read
Computational Advertising: Overview and Key Techniques from the Second Edition
DataFunTalk
DataFunTalk
Sep 10, 2019 · Big Data

Why We Should Ride the Big Data Carriage: Business Perspectives on Data Growth and Machine Learning

The article explains why businesses must embrace the rapid, non‑linear growth of data and machine‑learning technologies, illustrating how data volume and richer information can drive exponential business value, improve competitiveness, and create sustainable positive feedback loops across various industry scenarios.

AIDigital Transformationbig data
0 likes · 13 min read
Why We Should Ride the Big Data Carriage: Business Perspectives on Data Growth and Machine Learning
Architecture Digest
Architecture Digest
Sep 9, 2019 · Artificial Intelligence

Overview of Recommendation System Architecture, Algorithms, and Evaluation

This article provides a comprehensive introduction to recommendation systems, covering their definition, overall offline and online architectures, feature engineering, collaborative filtering, latent semantic models, ranking algorithms, and evaluation methods including A/B testing and offline metrics.

A/B testingcollaborative filteringfeature engineering
0 likes · 28 min read
Overview of Recommendation System Architecture, Algorithms, and Evaluation
Alibaba Cloud Developer
Alibaba Cloud Developer
Sep 9, 2019 · Big Data

Unlocking the Power of Unstructured Data: From AI Breakthroughs to Business Value

This article explains how unstructured data—comprising documents, images, audio, video and more—now dominates over 80% of all data, outlines its characteristics and challenges, compares it with structured data, and showcases real-world AI applications such as ImageNet, intelligent customer service and smart security, while proposing a roadmap for building a unified unstructured‑data asset.

Data Analyticsbig datamachine learning
0 likes · 15 min read
Unlocking the Power of Unstructured Data: From AI Breakthroughs to Business Value
Snowball Engineer Team
Snowball Engineer Team
Sep 4, 2019 · Artificial Intelligence

Advancing Recommendation Systems at Xueqiu: Transitioning from Point-Wise CTR Prediction to Pair-Wise TF-Ranking

This article explores the evolution of recommendation algorithms at Xueqiu, highlighting the limitations of traditional point-wise click-through rate prediction models and detailing the ongoing transition to a pair-wise TF-Ranking framework designed to mitigate user and content biases while significantly enhancing overall recommendation accuracy and user experience.

CTR predictionPair-Wise LearningTF-Ranking
0 likes · 5 min read
Advancing Recommendation Systems at Xueqiu: Transitioning from Point-Wise CTR Prediction to Pair-Wise TF-Ranking
DataFunTalk
DataFunTalk
Sep 3, 2019 · Big Data

The Value of Big Data in Machine Learning: Detailed Illustration and Insights

This article explains how big data enhances machine learning by enabling finer-grained data characterization, improving confidence in statistical conclusions, and supporting smarter learning through multiple stages of model development, illustrated with concrete examples and a discussion of sample size dilemmas.

Data Analysisbig datamachine learning
0 likes · 10 min read
The Value of Big Data in Machine Learning: Detailed Illustration and Insights
DataFunTalk
DataFunTalk
Sep 2, 2019 · Artificial Intelligence

Credit Risk Strategies: Data, Rules, and Model Development for Consumer Lending

This article presents a comprehensive overview of consumer credit risk management, covering industry background, traditional scoring‑card and machine‑learning model development processes, risk‑rate and limit strategies, rule effectiveness diagnostics, and advanced model‑optimization techniques to improve underwriting performance and cost efficiency.

Risk Modelingconsumer lendingcredit risk
0 likes · 10 min read
Credit Risk Strategies: Data, Rules, and Model Development for Consumer Lending
DataFunTalk
DataFunTalk
Aug 30, 2019 · Artificial Intelligence

TransFM: Integrating Translation-based Recommendation and Factorization Machines for Sequential Recommendation

This article reviews the TransFM model, which combines the translation‑based sequential recommendation approach (TransRec) with factorization machines (FM), explains its formulation, optimization via sequential Bayesian personalized ranking, and demonstrates its superior performance on Amazon and Google Local datasets compared with several baselines.

evaluationfactorization machinesmachine learning
0 likes · 8 min read
TransFM: Integrating Translation-based Recommendation and Factorization Machines for Sequential Recommendation
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 28, 2019 · Artificial Intelligence

Exact‑K Recommendation: Graph Attention Networks and RL from Demonstrations Explained

This article introduces the Exact‑K recommendation problem, highlights its differences from traditional Top‑K approaches, and presents a novel solution combining Graph Attention Networks (GAttN) with Reinforcement Learning from Demonstrations (RLfD), backed by extensive experiments showing superior performance on real-world datasets.

exact-kgraph attention networksmachine learning
0 likes · 14 min read
Exact‑K Recommendation: Graph Attention Networks and RL from Demonstrations Explained
DataFunTalk
DataFunTalk
Aug 27, 2019 · Artificial Intelligence

How Machines Learn: From Newton’s Second Law to the Core Steps of Supervised Learning

This article illustrates how a machine can rediscover Newton’s second law by treating force and acceleration data as a simple linear regression problem, detailing the three fundamental steps of hypothesis space definition, loss function design, and optimization through calculus or gradient methods.

Newton's lawOptimizationhypothesis space
0 likes · 15 min read
How Machines Learn: From Newton’s Second Law to the Core Steps of Supervised Learning
Amap Tech
Amap Tech
Aug 27, 2019 · Artificial Intelligence

POI Category Tagging: Multi‑Label Classification, Feature Engineering and Model Design

The system tackles POI category tagging as a multi‑label classification problem by engineering textual and non‑textual features, mining click‑log and external samples through active learning, and deploying hierarchical and per‑tag deep textCNN models with feature fusion, achieving over 5 % accuracy gain, ten‑fold speedup, and markedly higher precision and coverage that boost map‑search relevance.

POI taggingTextCNNfeature engineering
0 likes · 19 min read
POI Category Tagging: Multi‑Label Classification, Feature Engineering and Model Design
Youku Technology
Youku Technology
Aug 26, 2019 · Artificial Intelligence

Technical Deep Dive of Adaptive Streaming for “The Longest Day in Chang'an”: AI, Big Data, and QoE Optimization

Alibaba Entertainment’s technical deep‑dive reveals how its Smart Bitrate system leverages big‑data analytics, AI‑driven machine‑learning (including MPC and the Pensieve model) to dynamically select 5‑10‑second video segments, optimizing QoE by balancing high‑definition playback and buffering, achieving 5‑10% more HD viewing and 10‑20% fewer stalls for “The Longest Day in Chang’an.”

AIMPCQoE
0 likes · 10 min read
Technical Deep Dive of Adaptive Streaming for “The Longest Day in Chang'an”: AI, Big Data, and QoE Optimization
Tencent Cloud Developer
Tencent Cloud Developer
Aug 23, 2019 · Artificial Intelligence

WeChat Reading "Guess You Like" Recommendation System: Algorithms and Architecture

WeChat Reading’s “Guess You Like” engine combines real‑time click and reading data with tag‑based and deep‑learning embeddings, LSH similarity search, and a DeepFM ranking model to deliver cross‑type book, article, and video recommendations, continuously balancing exploitation and exploration to boost CTR and user engagement.

content-based filteringmachine learningrecommendation system
0 likes · 13 min read
WeChat Reading "Guess You Like" Recommendation System: Algorithms and Architecture
360 Quality & Efficiency
360 Quality & Efficiency
Aug 23, 2019 · Artificial Intelligence

High‑Performance High‑Dimensional Vector KNN Search Using FAISS

This article introduces the background of vector representations in machine learning, explains the K‑Nearest Neighbors algorithm and its key parameters, reviews traditional tree‑based and modern high‑performance search solutions, and demonstrates how FAISS can achieve microsecond‑level KNN queries on large‑scale high‑dimensional data.

FAISSKNNVector Search
0 likes · 5 min read
High‑Performance High‑Dimensional Vector KNN Search Using FAISS
DataFunTalk
DataFunTalk
Aug 22, 2019 · Artificial Intelligence

End‑to‑End Group Risk Perception Modeling: From Requirement Mining to Deployment

This article presents a comprehensive workflow for group risk perception, covering business requirement mining, data acquisition and understanding, feature engineering, model training and evaluation, deployment, and practical user applications, with detailed objectives, methods, and deliverables for each stage.

Risk Modelingdata mininggroup behavior analysis
0 likes · 11 min read
End‑to‑End Group Risk Perception Modeling: From Requirement Mining to Deployment
DataFunTalk
DataFunTalk
Aug 20, 2019 · Artificial Intelligence

The Story of Machine Learning: Why Machines Can Learn and How Statistical Learning Makes It Possible

This article explains why machine learning relies on big‑data statistical learning, illustrating human learning through induction and deduction, presenting case studies that highlight the limits of anecdotal reasoning, and introducing the law of large numbers and probabilistic trust as foundations for reliable AI models.

big datalearning theorymachine learning
0 likes · 19 min read
The Story of Machine Learning: Why Machines Can Learn and How Statistical Learning Makes It Possible
DataFunTalk
DataFunTalk
Aug 19, 2019 · Artificial Intelligence

Algorithmic Practices in Hulu's Video Advertising System

This article details how Hulu leverages machine learning and AI techniques—including ad targeting, inventory prediction, conversion rate optimization, causal inference, and real‑time bidding—to improve ad efficiency, user experience, and revenue across its video streaming platform.

AIAd TargetingAdvertising
0 likes · 15 min read
Algorithmic Practices in Hulu's Video Advertising System
Python Crawling & Data Mining
Python Crawling & Data Mining
Aug 16, 2019 · Fundamentals

Master NumPy: Essential Array and Matrix Operations for Data Science

This guide introduces NumPy's core features—including array creation, arithmetic, indexing, aggregation, multi‑dimensional handling, matrix operations, and practical examples such as computing mean‑square error—providing a comprehensive foundation for Python‑based data analysis and machine‑learning workflows.

Array OperationsData ScienceMatrix Computation
0 likes · 10 min read
Master NumPy: Essential Array and Matrix Operations for Data Science
Programmer DD
Programmer DD
Aug 16, 2019 · Artificial Intelligence

Can AI Hear the Sweetest Watermelon? Acoustic Detection Explained

An interdisciplinary study from Zhejiang University demonstrates how machine learning, especially LS‑SVM, can analyze acoustic signals from knocked watermelons to accurately classify their ripeness and internal hollowness, offering a low‑cost, efficient alternative to traditional subjective methods and boosting watermelon quality assessment.

AILS-SVMacoustic detection
0 likes · 11 min read
Can AI Hear the Sweetest Watermelon? Acoustic Detection Explained
DataFunTalk
DataFunTalk
Aug 15, 2019 · Artificial Intelligence

Intelligent Customer Acquisition System Practice at Du Xiaoman Financial

This article presents a comprehensive overview of Du Xiaoman Financial's intelligent customer acquisition system, covering acquisition channels, efficiency improvements through multi‑stage models, data understanding with deepFM, the platform architecture, and related recruitment for senior machine‑learning engineers.

AICustomer AcquisitionFinancial Technology
0 likes · 9 min read
Intelligent Customer Acquisition System Practice at Du Xiaoman Financial
DataFunTalk
DataFunTalk
Aug 14, 2019 · Artificial Intelligence

Understanding Recommendation Systems: From Information Overload to Personalized AI Solutions

The article explores how the rapid growth of the internet has created information overload, discusses the challenges of recommendation systems such as sparsity and timeliness, outlines a four‑step personalized content pipeline, and highlights the interdisciplinary nature of building effective AI‑driven recommendation solutions.

AIbig datadata engineering
0 likes · 16 min read
Understanding Recommendation Systems: From Information Overload to Personalized AI Solutions
Youku Technology
Youku Technology
Aug 12, 2019 · Artificial Intelligence

Interpretation of the Paper “Multi-View Multi-Label Learning with View‑Specific Information Extraction” (SIMM)

The article explains SIMM, a neural‑network framework for multi‑view multi‑label learning that jointly extracts a shared, view‑invariant subspace via adversarial loss and orthogonal view‑specific features, demonstrating superior performance across eight benchmark datasets compared to existing MVML and ML‑kNN methods.

AIMulti-view learningadversarial learning
0 likes · 11 min read
Interpretation of the Paper “Multi-View Multi-Label Learning with View‑Specific Information Extraction” (SIMM)
360 Tech Engineering
360 Tech Engineering
Aug 8, 2019 · Artificial Intelligence

Recommendation System Optimization: Lessons, AB Testing Cycles, and Practical Principles

This article shares extensive practical experience on recommendation system optimization, outlining the importance of problem definition, the limits of AB testing, and four guiding principles—avoid fundamentally wrong actions, do the right things correctly, keep solutions simple, and prevent over‑optimization.

A/B testingAlgorithm EngineeringSystem Design
0 likes · 9 min read
Recommendation System Optimization: Lessons, AB Testing Cycles, and Practical Principles
DataFunTalk
DataFunTalk
Aug 8, 2019 · Artificial Intelligence

Practical Experience of JD E‑commerce Recommendation System: Architecture, Ranking, Real‑time Updates, and Experiment Platform

This article shares JD's e‑commerce recommendation system practice, covering the overall online/offline architecture, recall and ranking modules, real‑time feature and model updates, multi‑objective and diversity strategies, first‑stage index‑based ranking, KNN recall, and a layered experiment platform for rapid iteration.

e-commercelearning to rankmachine learning
0 likes · 14 min read
Practical Experience of JD E‑commerce Recommendation System: Architecture, Ranking, Real‑time Updates, and Experiment Platform
Amap Tech
Amap Tech
Aug 6, 2019 · Artificial Intelligence

Boosting ETA Accuracy: How TCN Improves Historical Speed Prediction for Navigation

To enhance estimated arrival times in navigation, this article analyzes the shortcomings of traditional historical average methods and proposes a machine‑learning solution using Temporal Convolutional Networks combined with dynamic and static feature engineering, demonstrating reduced bad‑case rates and better handling of seasonal patterns.

ETA predictionTCNTime Series
0 likes · 11 min read
Boosting ETA Accuracy: How TCN Improves Historical Speed Prediction for Navigation
21CTO
21CTO
Jul 31, 2019 · Artificial Intelligence

How JD Built a Scalable AI‑Powered Recommendation System

The article outlines JD’s evolution from rule‑based product suggestions in 2012 to a sophisticated, AI‑driven, multi‑screen personalized recommendation platform, detailing its product types, system architecture, data collection, offline and online computation, and the core recommendation engine that powers features like “Guess You Like.”

AIJD.combig data
0 likes · 14 min read
How JD Built a Scalable AI‑Powered Recommendation System
Architecture Digest
Architecture Digest
Jul 30, 2019 · Artificial Intelligence

Evolution and Architecture of JD.com’s Personalized Recommendation System

The article details JD.com’s journey from rule‑based product recommendations in 2012 to a sophisticated, AI‑driven personalized recommendation system, describing its multi‑screen product types, data collection, offline and online computation pipelines, and the modular architecture of its recommendation engine.

JD.come-commercemachine learning
0 likes · 12 min read
Evolution and Architecture of JD.com’s Personalized Recommendation System
Efficient Ops
Efficient Ops
Jul 28, 2019 · Operations

How 58’s Intelligent Monitoring System Guarantees 24/7 Service Stability

This article details the design, architecture, and AI‑driven features of 58’s intelligent monitoring platform, explaining how multi‑dimensional data collection, predictive analytics, and smart alarm merging ensure continuous, automated observability across network, server, application, and business layers.

Cloud Infrastructureanomaly detectionmachine learning
0 likes · 20 min read
How 58’s Intelligent Monitoring System Guarantees 24/7 Service Stability
DataFunTalk
DataFunTalk
Jul 26, 2019 · Artificial Intelligence

Hulu’s Video Content Understanding: Challenges, Practices, and Applications

This article summarizes Hulu Chief Research Officer Xie Xiaohui’s presentation on why video content understanding is essential, the technical challenges involved, and Hulu’s end‑to‑end solutions—including fine‑grained segmentation, logo and subtitle detection, automated pipelines, tagging taxonomy, content generation, and vector embeddings—to improve recommendation, advertising, and search for massive video libraries.

AIHulucontent tagging
0 likes · 14 min read
Hulu’s Video Content Understanding: Challenges, Practices, and Applications
DataFunTalk
DataFunTalk
Jul 25, 2019 · Artificial Intelligence

Understanding Recommendation Ranking: Definition, Decomposition, and Modeling

This article introduces recommendation ranking, breaks down its components, explains how to build ranking models, and provides additional insights, while also presenting the author's background and related job opportunities in the e‑commerce recommendation field.

AIe-commercemachine learning
0 likes · 3 min read
Understanding Recommendation Ranking: Definition, Decomposition, and Modeling
JD Tech Talk
JD Tech Talk
Jul 24, 2019 · Artificial Intelligence

Absolute Semantic Recognition Competition: Feature Design, Modeling Strategy, and Core Algorithm Insights

This article presents a comprehensive solution to the absolute semantic recognition competition, detailing the problem background, dataset, evaluation metrics, feature engineering, model architecture—including Attention, Capsule, Bi‑GRU, and BERT—and analysis of results and lessons learned.

AttentionBERTCapsule Networks
0 likes · 11 min read
Absolute Semantic Recognition Competition: Feature Design, Modeling Strategy, and Core Algorithm Insights
Tencent Cloud Developer
Tencent Cloud Developer
Jul 19, 2019 · Artificial Intelligence

Multi-turn Dialogue Intent Classification: Data Processing, Model Construction, and Operational Optimization

The article details a multi‑turn dialogue intent classification pipeline that extracts and expands labeled utterances, preprocesses text with custom tokenization, trains a two‑layer CNN‑Highway and a multi‑head self‑attention model, analyzes errors, and achieves up to 98.7% accuracy on a large, balanced dataset.

BERTCNNText classification
0 likes · 15 min read
Multi-turn Dialogue Intent Classification: Data Processing, Model Construction, and Operational Optimization
DataFunTalk
DataFunTalk
Jul 19, 2019 · Artificial Intelligence

From the Pre‑Recommendation Era to the Bronze Age: Evolution of Recommendation Systems and Mitigating the Matthew Effect

The article traces the historical development of recommendation systems from early manual and hot‑ranking methods through natural ranking and machine‑learning‑based scoring, discusses the Matthew effect and its mitigation via randomization, multi‑objective weighting, and pipeline architectures, and outlines modern personalization and recall strategies for e‑commerce platforms.

Algorithmsdatae-commerce
0 likes · 25 min read
From the Pre‑Recommendation Era to the Bronze Age: Evolution of Recommendation Systems and Mitigating the Matthew Effect
Amap Tech
Amap Tech
Jul 16, 2019 · Artificial Intelligence

Mobile Wi‑Fi Identification for Enhanced Network Positioning Using Machine Learning

By replacing rule‑based pipelines with an active‑learning‑driven random‑forest model that extracts clustering, signal, association, IP, and temporal features, Gaode accurately identifies mobile, cloned, and moved Wi‑Fi, cutting large‑error network‑positioning cases by ~18% and boosting overall positioning precision.

Random ForestWiFi fingerprintingmachine learning
0 likes · 13 min read
Mobile Wi‑Fi Identification for Enhanced Network Positioning Using Machine Learning
DataFunTalk
DataFunTalk
Jul 11, 2019 · Artificial Intelligence

Alibaba Retail's Intelligent Recommendation System: Business Background, Architecture, Matching and Ranking Models

This article presents a comprehensive overview of Alibaba Retail's B2B2C intelligent recommendation platform, detailing its business context, three core recommendation scenarios, system architecture, matching algorithms such as item‑CF, graph embedding and user‑CF, as well as the evolution of ranking models and feature engineering practices.

AlibabaB2B2Ce-commerce
0 likes · 17 min read
Alibaba Retail's Intelligent Recommendation System: Business Background, Architecture, Matching and Ranking Models
Alibaba Cloud Developer
Alibaba Cloud Developer
Jul 9, 2019 · Artificial Intelligence

Demystifying Attention: A Clear Guide to Its History, Types, and Why It Works

This article systematically reviews the evolution of attention mechanisms—from early additive and multiplicative forms to self‑attention and multi‑head variants—explaining their core three‑step framework, key differences, and why they have become essential across NLP, vision, and broader AI applications.

NLPTransformerdeep learning
0 likes · 19 min read
Demystifying Attention: A Clear Guide to Its History, Types, and Why It Works
DataFunTalk
DataFunTalk
Jul 5, 2019 · Artificial Intelligence

Lead Quality Prediction for Real Estate: Data, Modeling, and Interpretability

This article presents a case study on building and deploying a lead‑quality classification model for a high‑value, low‑frequency real‑estate platform, covering business context, data challenges, sampling strategies, feature engineering, model selection, tuning, evaluation metrics, interpretability analysis, and observed performance improvements.

Classificationlead scoringmachine learning
0 likes · 14 min read
Lead Quality Prediction for Real Estate: Data, Modeling, and Interpretability
Suning Technology
Suning Technology
Jul 3, 2019 · Artificial Intelligence

Debunking Common AI Myths: What Every Business Should Know

This article dispels five widespread AI misconceptions—from believing AI works like the human brain to thinking it is bias‑free—while offering practical guidance on recognizing AI limits, improving data quality, managing risks, and applying AI responsibly across industries.

AIData QualityRisk Management
0 likes · 13 min read
Debunking Common AI Myths: What Every Business Should Know
HomeTech
HomeTech
Jul 3, 2019 · Artificial Intelligence

Applying Sequence Embedding for Car Model Preference Prediction

This article explains how sequence embedding can be applied to user browsing data on an automotive website to predict intended car models, addressing data sparsity and leveraging temporal information to improve prediction accuracy by 3%.

Artificial Intelligencecar recommendationmachine learning
0 likes · 6 min read
Applying Sequence Embedding for Car Model Preference Prediction
DataFunTalk
DataFunTalk
Jul 3, 2019 · Artificial Intelligence

Improving Recommendation Diversity with Determinantal Point Processes and Greedy Optimization

The article explains how recommendation systems balance exploitation and exploration, introduces diversity metrics such as temporal, spatial, and coverage, and presents a determinantal point process (DPP) based algorithm accelerated by Cholesky decomposition and greedy inference, demonstrating significant speedups and improved relevance‑diversity trade‑offs in experiments.

Optimizationcholesky decompositiondeterminantal point process
0 likes · 10 min read
Improving Recommendation Diversity with Determinantal Point Processes and Greedy Optimization
58 Tech
58 Tech
Jul 2, 2019 · Artificial Intelligence

Magic Mirror: A Visual Data‑Intelligence Platform for Low‑Code Machine Learning

Magic Mirror is a big‑data‑based visual analytics platform that lowers the barrier of machine‑learning for non‑experts while accelerating expert workflows through visual UI, modular algorithms, distributed feature generation, and automated binary‑classification modeling.

Automated ModelingSparkbig data
0 likes · 9 min read
Magic Mirror: A Visual Data‑Intelligence Platform for Low‑Code Machine Learning
dbaplus Community
dbaplus Community
Jun 27, 2019 · Artificial Intelligence

How AI Powers Intelligent Multi-Modal Financial Data Quality Monitoring

This article presents the design, implementation, and evaluation of X‑monitor, an AI‑driven, adaptive, multi‑modal financial data quality monitoring platform that combines rule‑based and self‑learning strategies to improve detection efficiency, accuracy, and flexibility for large‑scale securities‑firm data streams.

AIbig-datadata-quality
0 likes · 24 min read
How AI Powers Intelligent Multi-Modal Financial Data Quality Monitoring
DataFunTalk
DataFunTalk
Jun 25, 2019 · Artificial Intelligence

Embedding‑Based Item‑to‑Item Recommendation for Homestay Platforms

This article describes how Tujia applied embedding techniques, particularly a Skip‑Gram model, to build an item‑to‑item similarity recommender for low‑frequency, highly personalized homestay listings, detailing the data preparation, model architecture, training process, evaluation results, practical improvements, and future directions.

AB testEmbeddingSkip-gram
0 likes · 13 min read
Embedding‑Based Item‑to‑Item Recommendation for Homestay Platforms
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 25, 2019 · Artificial Intelligence

How Alibaba Inserts Marketing Cards to Boost Recommendation Revenue

This article explains how Alibaba's App recommendation pipeline integrates marketing scenario cards using weak personalization and machine‑learning models, detailing the metrics, feature engineering, recall and ranking strategies that together raise exposure revenue and click‑through performance.

AlibabaCTR predictioncard insertion
0 likes · 8 min read
How Alibaba Inserts Marketing Cards to Boost Recommendation Revenue
Amap Tech
Amap Tech
Jun 21, 2019 · Artificial Intelligence

Improving Origin Snap Accuracy in Amap Navigation Using Machine Learning

To overcome the limitations of handcrafted rules for binding users’ reported start locations to the correct road segment, Amap built a data‑driven, list‑wise learning‑to‑rank model that leverages real‑travel and planning data, achieving a 10 % error reduction and 40 % accuracy gain on difficult origin‑snapping cases.

Map Navigationfeature engineeringmachine learning
0 likes · 10 min read
Improving Origin Snap Accuracy in Amap Navigation Using Machine Learning
360 Tech Engineering
360 Tech Engineering
Jun 14, 2019 · Information Security

A Guide to Producing Threat Intelligence from a Security Analysis Perspective

This article explains how threat intelligence is generated by defining it as judged security information, outlines methods for collecting and evaluating security data, introduces a two‑dimensional reliability/quality rating system, and provides a step‑by‑step engineering workflow for enterprise threat‑intelligence operations.

information collectionmachine learningrisk assessment
0 likes · 10 min read
A Guide to Producing Threat Intelligence from a Security Analysis Perspective
DataFunTalk
DataFunTalk
Jun 12, 2019 · Artificial Intelligence

Credit Scoring Cards vs Machine Learning in Financial Risk Control: Comparative Analysis and Practical Applications

The article compares traditional credit‑scoring‑card models with modern machine‑learning approaches for financial risk control, detailing feature selection criteria, non‑linear handling, data characteristics, practical ML techniques, large‑scale modeling challenges, and summarizing insights for future development.

Risk Modelingfinancial riskmachine learning
0 likes · 14 min read
Credit Scoring Cards vs Machine Learning in Financial Risk Control: Comparative Analysis and Practical Applications
DataFunTalk
DataFunTalk
Jun 4, 2019 · Artificial Intelligence

Study Notes on "Computational Advertising": Overview, System Architecture, Targeted & Online Ads, E&E Algorithm

This article presents detailed study notes on the book “Computational Advertising”, covering an overview, ad system architecture, targeted advertising, online advertising, the E‑E algorithm, and additional insights, accompanied by illustrative diagrams to aid understanding of modern advertising technologies.

E&E algorithmOnline Advertisingad systems
0 likes · 3 min read
Study Notes on "Computational Advertising": Overview, System Architecture, Targeted & Online Ads, E&E Algorithm