Tagged articles

user profiling

129 articles · Page 2 of 2

Jun 13, 2018 · Artificial Intelligence

Evolution of E‑commerce Platform Recommendation System Architecture

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

Real-timearchitecturee-commerce

0 likes · 10 min read

Evolution of E‑commerce Platform Recommendation System Architecture

JD Retail Technology

Apr 20, 2018 · Big Data

JD.com's Unbounded Marketing Platform Wins Big Data Product Award

JD.com's Unbounded Marketing Platform won the Big Data Product Award at the 2018 Big Data Industry Summit, showcasing its innovative data-driven marketing solutions for brands and consumers.

big data marketingconversion optimizationcross-platform marketing

0 likes · 4 min read

JD.com's Unbounded Marketing Platform Wins Big Data Product Award

360 Quality & Efficiency

Feb 5, 2018 · Artificial Intelligence

Fundamentals of Recommendation Engines: User Profiling, Data Classification, and Testing Methods

The article explains the core concepts of recommendation engines—user profiling and data classification—describes how large‑scale data processing tools are used to build models, and outlines common offline and A/B testing approaches for evaluating recommendation performance.

AB testingOffline Testingdata classification

0 likes · 4 min read

Fundamentals of Recommendation Engines: User Profiling, Data Classification, and Testing Methods

Architecture Digest

Jan 30, 2018 · Artificial Intelligence

Overview of Toutiao's Recommendation System: Architecture, Content Analysis, User Tagging, Evaluation, and Content Safety

This article presents a comprehensive overview of Toutiao's recommendation system, detailing its three‑dimensional modeling approach, real‑time training pipeline, feature engineering, content and user analysis techniques, evaluation methodology, and the extensive content‑safety mechanisms employed to ensure reliable and responsible information distribution.

Content SafetyEvaluationcontent analysis

0 likes · 19 min read

Overview of Toutiao's Recommendation System: Architecture, Content Analysis, User Tagging, Evaluation, and Content Safety

21CTO

Jan 16, 2018 · Artificial Intelligence

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

This article provides a comprehensive overview of Toutiao's recommendation system, covering its three‑dimensional modeling approach, feature engineering, real‑time training pipeline, recall strategies, user‑tag generation, evaluation methodology, and content‑safety mechanisms.

Content SafetyEvaluationReal-time Training

0 likes · 18 min read

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

Meituan Technology Team

Dec 1, 2017 · Artificial Intelligence

Meituan-Dianping DSP Advertising Coarse Ranking Mechanisms and Scenario‑Based Targeting

Meituan‑Dianping’s DSP coarse‑ranking filters large ad candidate sets by scoring ads with user‑profile, weather, and keyword scenario models—using frequent‑itemset mining, AdaBoost, and TF/IDF—then aggregates these scores via a linear‑regression model to select high‑relevance ads for fine‑ranking, boosting click‑through and conversion rates.

Advertisingcoarse rankingkeyword targeting

0 likes · 23 min read

Meituan-Dianping DSP Advertising Coarse Ranking Mechanisms and Scenario‑Based Targeting

Baixing.com Technical Team

Nov 30, 2017 · Artificial Intelligence

How User Profiling Powers Modern Recommendation Systems

This article explains what user profiling is, why it’s crucial for recommendation systems, outlines key dimensions such as personal attributes, status, and interests, describes algorithms like classification and autoregressive models, and details offline and real‑time computation methods, evaluation techniques, and practical examples.

Recommendation Systemsalgorithmdata mining

0 likes · 11 min read

How User Profiling Powers Modern Recommendation Systems

Baixing.com Technical Team

Nov 28, 2017 · Artificial Intelligence

How Recommendation Systems Work: Architecture, Flow, and Evaluation

This article explains what recommendation systems are, their purpose, differences from search, typical multi‑layer architecture, real‑time and offline processing flows, user feedback handling, and key metrics for evaluating their effectiveness.

Evaluation MetricsRecommendation Systemsmachine learning

0 likes · 14 min read

How Recommendation Systems Work: Architecture, Flow, and Evaluation

ITPUB

Nov 12, 2017 · Information Security

How E‑commerce Platforms Fight Double‑11 Fraud: Inside NetEase’s Anti‑Cheat Architecture

This article examines the rise of organized “wool‑pulling” fraud groups during China’s Double‑11 shopping festival, outlines their tools and tactics, and details NetEase Cloud Security’s multi‑layered anti‑fraud system—including captcha, SMS verification, IP rules, device fingerprinting, rule engines, user profiling, network graph analysis, and blacklist strategies—to protect e‑commerce platforms.

Double 11Rule Engineanti-fraud

0 likes · 16 min read

How E‑commerce Platforms Fight Double‑11 Fraud: Inside NetEase’s Anti‑Cheat Architecture

21CTO

Sep 27, 2017 · Artificial Intelligence

How Tagging and User Profiling Power Modern Recommendation Systems

This article explores how simple tagging and user profiling underpin modern recommendation systems, contrasting tag‑based, flexible representations with traditional hierarchical classifications, and examines practical applications such as personalized advertising, industry research, and product optimization.

Recommendation SystemsTaggingdata mining

0 likes · 13 min read

How Tagging and User Profiling Power Modern Recommendation Systems

21CTO

Aug 18, 2017 · Big Data

How Ctrip Builds a Scalable User Profile Platform for Personalized Travel

This article explains why Ctrip creates user profiles, describes the product and technical architectures, and details the data collection, computation, storage, high‑availability querying, and monitoring components that power its personalized travel recommendations and services.

CtripReal-time Processingdata pipeline

0 likes · 8 min read

Qunar Tech Salon

May 16, 2017 · Artificial Intelligence

Personalized Recommendation Systems: Applications, User Profiling, Algorithms, and Optimization

This article presents a comprehensive overview of personalized recommendation systems, covering their application scenarios and value, user profiling, core algorithms such as content‑based and collaborative filtering, system architecture, performance and effect optimization techniques, and practical Q&A insights.

AIBig Datacollaborative filtering

0 likes · 18 min read

Personalized Recommendation Systems: Applications, User Profiling, Algorithms, and Optimization

Baidu Waimai Technology Team

Mar 21, 2017 · Backend Development

Automated Testing Framework for Baidu Waimai User Profiling Using Asynchronous Coroutines

This article describes how Baidu Waimai’s user‑profile offline data system was equipped with a highly automated, coroutine‑based testing framework that dramatically improves field‑value accuracy verification, test coverage, and execution efficiency across strategy, ES, and API layers.

Baidu WaimaiData Qualityasynchronous coroutines

0 likes · 9 min read

Automated Testing Framework for Baidu Waimai User Profiling Using Asynchronous Coroutines

Meituan Technology Team

Feb 17, 2017 · Big Data

User Profiling and Machine Learning Practices for Food Delivery O2O Platforms

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

Big DataO2Ochurn prediction

0 likes · 13 min read

User Profiling and Machine Learning Practices for Food Delivery O2O Platforms

Meitu Technology

Dec 1, 2016 · Big Data

Multi-dimensional Analysis Platform Based on User Portrait Data

Tencent's Glacier multi‑dimensional analysis platform combines massive user‑portrait tags with routine analytical reports, delivering fast, accurate real‑time queries across countless dimensional combinations, enabling analysts and operators to perform targeted operations and insights as product data continuously evolves.

Big DataData PlatformGlacier

0 likes · 1 min read

Multi-dimensional Analysis Platform Based on User Portrait Data

Meitu Technology

Dec 1, 2016 · Big Data

Meitu Internet Technology Salon: Big Data Architecture Evolution and Practice, and Tencent Multi‑Dimensional Analysis Platform

At Meitu’s third Internet Technology Salon in Xiamen on November 26 2016, over 150 senior engineers heard Meitu’s Lu Rongbin detail the company’s progression from simple rsync scripts to a scalable mobile data and open statistical platform, while Tencent’s Zhao Shiyuan showcased the Glacier multi‑dimensional analysis system for fast, tag‑driven queries, underscoring collaborative technical exchange in South China.

AnalyticsBig DataData Platform

0 likes · 6 min read

Meitu Internet Technology Salon: Big Data Architecture Evolution and Practice, and Tencent Multi‑Dimensional Analysis Platform

Architects' Tech Alliance

Nov 28, 2016 · Big Data

User Profiling: Concepts, Stages, and Data Modeling Methods

This article explains the concept of user profiling, outlines its four-stage construction process, discusses the significance of tagging users, and details practical data modeling techniques—including static and dynamic data sources, weight calculations, and real‑world examples—aimed at improving precision marketing and recommendation systems.

Big DataTaggingbehavior analysis

0 likes · 44 min read

User Profiling: Concepts, Stages, and Data Modeling Methods

Tencent Music Tech Team

Nov 4, 2016 · Artificial Intelligence

How QQ Music Recommendation System Understands Your Preferences

The QQ Music recommendation system tackles cold‑start by first mixing Chinese and English tracks, then builds a six‑dimensional user profile (content, social, scenario, crowd, time, blacklist) and tags songs with six attributes, using content‑based, collaborative, matrix‑factorization and neural‑network models plus implicit co‑listening links, while acknowledging that final wisdom still comes from human listeners.

cold-startcollaborative filteringmusic recommendation

0 likes · 11 min read

How QQ Music Recommendation System Understands Your Preferences

21CTO

Aug 24, 2016 · Artificial Intelligence

How User Profiling Powers Modern Recommendation Systems

This article explains how comprehensive user profiling—combining static demographics and dynamic behavior logs—feeds recommendation engines, detailing data sources, feature extraction, ranking formulas, and the long‑term goals of delivering personalized, high‑quality content to users.

data analysispersonalizationrecommendation system

0 likes · 6 min read

ITPUB

Jun 11, 2016 · Big Data

How 58 Daojia Leverages User Portraits to Boost Operations and Fight Fraud

This article details 58 Daojia's data‑driven approach to building user‑portrait tags, covering tag construction, evaluation, and practical applications such as personalized recommendations, anti‑fraud measures, coupon distribution, and dynamic pricing, while outlining the underlying big‑data architecture and technical challenges.

Big Dataanti-frauddata mining

0 likes · 18 min read

How 58 Daojia Leverages User Portraits to Boost Operations and Fight Fraud

Baidu Intelligent Testing

May 4, 2016 · Big Data

Understanding Big Data: The Importance of Data Breadth and User Profiling for Precise Marketing and Product Optimization

The article explains the core concepts of big data, emphasizing data breadth across product lines, illustrates how comprehensive user profiling can drive personalized marketing and product improvements, and provides practical examples of cross‑product data analysis in e‑commerce, finance, travel, and gaming contexts.

Big Datacross‑product analysisdata breadth

0 likes · 5 min read

Understanding Big Data: The Importance of Data Breadth and User Profiling for Precise Marketing and Product Optimization

21CTO

Apr 14, 2016 · Big Data

How Meituan’s Data Architecture Powers Precise Mobile Marketing

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

Big DataData Architecturemachine learning

0 likes · 12 min read

How Meituan’s Data Architecture Powers Precise Mobile Marketing

Meituan Technology Team

Apr 14, 2016 · Big Data

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

Meituan‑Dianping’s data‑driven precise‑marketing platform combines a layered pyramid architecture—data warehouse, service, and front‑end layers—with real‑time profile services powered by Redis and Elasticsearch, offering tools such as Hoek, Cord, and Cloud/Star to automate audience selection, coupon recommendation, and KPI monitoring, illustrated by food‑delivery user discovery and WeChat red‑packet coupon case studies, and guided by principles of reusable models and SOA decoupling.

Case StudyData Architectureprecise marketing

0 likes · 9 min read

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

21CTO

Jan 6, 2016 · Artificial Intelligence

How to Build an End‑to‑End Marketplace Recommendation System: Product, Algorithms & Implementation

This article walks through designing and implementing a full‑stack recommendation system for 58转转, covering product frameworks, user and item profiling, RFM modeling, personalized tagging, classification‑based and collaborative‑filtering approaches, and practical deployment tips.

RFM modelclassificationcollaborative filtering

0 likes · 8 min read

How to Build an End‑to‑End Marketplace Recommendation System: Product, Algorithms & Implementation

Architects Research Society

Dec 20, 2015 · Artificial Intelligence

Understanding Personalized Recommendation: Meaning, Differences, Scenarios, and Implementation

This article explains the significance of personalized recommendation, distinguishes it from traditional push services, outlines typical application scenarios, and details a step‑by‑step approach—including user profiling, behavior sampling, algorithm modeling, machine learning, and content lifecycle management—to build effective recommender systems.

information overloadpersonalized recommendationrecommender systems

0 likes · 7 min read

Understanding Personalized Recommendation: Meaning, Differences, Scenarios, and Implementation

Architects Research Society

Dec 17, 2015 · Artificial Intelligence

How Search Engine Experience Informs Personalized Recommendation at Toutiao

The article explains how search engine techniques such as large‑scale candidate recall, fine‑grained ranking, user profiling, and multi‑objective optimization are applied to news personalization at Toutiao, highlighting data sampling, machine‑learning pipelines, challenges of news freshness, and architectural evolution.

Search Enginemulti-objective optimizationnews recommendation

0 likes · 5 min read

How Search Engine Experience Informs Personalized Recommendation at Toutiao

21CTO

Dec 7, 2015 · Information Security

How Tencent Combats Fraudsters with Big Data and AI‑Powered Risk Engines

This article explains how Tencent uses big‑data collection, user profiling, and AI‑driven risk learning engines to detect and block malicious accounts, proxy IPs, and fraudulent activities across e‑commerce and other platforms, detailing the architecture, algorithms, and practical defenses employed.

Big Dataanti-fraudfraud detection

0 likes · 14 min read

How Tencent Combats Fraudsters with Big Data and AI‑Powered Risk Engines

Efficient Ops

Nov 26, 2015 · Big Data

Expert Insights on User Profiling and Stream Processing in Big Data

This article presents expert Q&A on effective user behavior analysis techniques for building detailed user profiles and compares mainstream stream‑processing solutions, outlining key factors such as latency, throughput, parallelism, and fault tolerance for selecting the right real‑time data platform.

Big Datastream processinguser profiling

0 likes · 11 min read

Expert Insights on User Profiling and Stream Processing in Big Data

Suning Design

Jul 17, 2014 · Mobile Development

What’s Next for Mobile Search? Exploring Future Input, Data, and Output Innovations

Mobile search is evolving beyond traditional keyword queries, with emerging trends in precise user profiling, crowdsourced data, voice and natural language understanding, deep linking, machine learning, and structured, intelligent result aggregation, promising a more personalized, context‑aware, and seamless search experience on smartphones.

deep linkingmachine learningmobile search

0 likes · 9 min read