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165 articles

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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 testEmbeddingMachine Learning

0 likes · 13 min read

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

JD Retail Technology

Jun 15, 2019 · Artificial Intelligence

Comprehensive 6.18 Preparation: Load Testing, Deep Personalization, and Recommendation Algorithm Optimizations

The department’s extensive 6.18 preparation involved systematic load‑testing, deep learning‑driven personalization of search recommendations, and multiple algorithmic enhancements to improve relevance and conversion, supported by detailed planning, cross‑team coordination, and dedicated night‑shift logistics.

AIAlgorithm OptimizationRecommendation Systems

0 likes · 6 min read

Comprehensive 6.18 Preparation: Load Testing, Deep Personalization, and Recommendation Algorithm Optimizations

DataFunTalk

Mar 15, 2019 · Artificial Intelligence

Designing Personalized, Dynamic, and Multimodal Knowledge Graphs for Chatbots

The article explores how chatbots require personalized dense knowledge graphs, dynamic temporal graphs, subjective emotion modeling, integration with external services, and multimodal media support, while also promoting a new NLP book and a related giveaway for readers.

AIChatbotDynamic Graph

0 likes · 9 min read

Designing Personalized, Dynamic, and Multimodal Knowledge Graphs for Chatbots

DataFunTalk

Mar 11, 2019 · Artificial Intelligence

Practical Implementation of Personalized Recommendation Systems: Overview, Algorithms, Challenges, and Architecture

This article presents a comprehensive overview of personalized recommendation systems, covering their purpose, common algorithms, development challenges, the multi‑layer architecture used at DataGrand, optimization techniques, and the range of services offered to enterprise customers.

Big DataMachine Learningcollaborative filtering

0 likes · 18 min read

Practical Implementation of Personalized Recommendation Systems: Overview, Algorithms, Challenges, and Architecture

Sohu Tech Products

Mar 6, 2019 · Artificial Intelligence

Applying Word2Vec Embeddings to Rental and News Recommendation: Model, Hyper‑parameters, and Optimization

This article explains the fundamentals of the Word2Vec SGNS model, details its hyper‑parameters and training tricks, and demonstrates how customized embeddings are built for rental‑listing and news‑article recommendation, covering data preparation, objective‑function redesign, evaluation, and deployment in both recall and ranking stages.

EmbeddingSGNSWord2Vec

0 likes · 14 min read

Applying Word2Vec Embeddings to Rental and News Recommendation: Model, Hyper‑parameters, and Optimization

iQIYI Technical Product Team

Dec 14, 2018 · Artificial Intelligence

AI Applications in Modern Technology and Society

The podcast examines AI’s rapid integration into entertainment, security and personalization, highlighting its use in automated video editing, facial-recognition tagging of celebrities and non-celebrities, while debating ethical concerns such as echo-chambers, emotional nuance, and the technology’s transformative yet limited role across industries.

AIData AnalysisEntertainment

0 likes · 7 min read

AI Applications in Modern Technology and Society

Beike Product & Technology

Dec 13, 2018 · Artificial Intelligence

Evolution and Architecture of Beike's Intelligent Real Estate Recommendation Platform

The talk details how Beike's senior algorithm expert Xu Yansong designed, iterated, and scaled a multi‑stage intelligent recommendation platform for real‑estate, covering service upgrades, personalized algorithms such as collaborative filtering and user profiling, modular architecture, stability engineering, data feedback loops, and future AI‑driven enhancements.

AISystem Architecturecollaborative filtering

0 likes · 17 min read

Evolution and Architecture of Beike's Intelligent Real Estate Recommendation Platform

58UXD

Dec 4, 2018 · Product Management

How 58.com Reimagined Its Homepage: From User‑Centric Design to a Youthful Brand Experience

This article details 58.com’s comprehensive homepage redesign, covering the shift from a functional layout to a youthful, emotionally resonant brand image through design thinking, visual language evolution, information architecture optimization, and personalized content strategies aimed at boosting user engagement and efficiency.

brandinghomepage redesigninformation architecture

0 likes · 9 min read

DataFunTalk

Nov 21, 2018 · Artificial Intelligence

Personalized Recommendation System of 51 Credit Card: Architecture, Challenges, and Growth Cases

This article details how 51 Credit Card leverages artificial intelligence to build a personalized recommendation system, covering business pain points, technical challenges, a three‑layer tagging architecture from bill and app data, model deployment pipelines, and real‑world growth case studies that boosted conversion and ROI.

AIFinanceMachine Learning

0 likes · 14 min read

Personalized Recommendation System of 51 Credit Card: Architecture, Challenges, and Growth Cases

21CTO

Sep 28, 2018 · Artificial Intelligence

Inside E‑Commerce Recommendation Systems: From Data Collection to Real‑Time Personalization

This article explains how e‑commerce recommendation systems work, covering regular and personalized recommendation types, the challenges of user profiling and data handling, the three‑stage recommendation pipeline, and the overall system architecture that powers real‑time, AI‑driven product suggestions.

AIdata pipelinee-commerce

0 likes · 17 min read

Inside E‑Commerce Recommendation Systems: From Data Collection to Real‑Time Personalization

21CTO

Sep 24, 2018 · Artificial Intelligence

Why Recommendation Algorithms Aren’t Magic: A Practical Guide

This article explains the fundamentals of recommendation algorithms, illustrates their modest impact with real‑world examples, and outlines how modern e‑commerce systems collect data, rank items, and use rapid A/B testing to continuously improve personalized recommendations.

A/B testingMachine LearningRecommendation Systems

0 likes · 10 min read

Why Recommendation Algorithms Aren’t Magic: A Practical Guide

Xianyu Technology

Aug 31, 2018 · Artificial Intelligence

Personalized Recommendation for Xianyu Small Item Pools: Challenges and Solutions

Xianyu’s personalized recommendation system struggles with tiny, fast‑turnover item pools because traditional X2I matrices provide insufficient recall, so the team introduced pool‑specific pre‑filtering, high‑dimensional vector search, and a real‑time search‑engine recall, the latter boosting clicks by 14 % and transactions by 0.14 %.

EngineeringXianyupersonalization

0 likes · 15 min read

Personalized Recommendation for Xianyu Small Item Pools: Challenges and Solutions

Alibaba Cloud Developer

Jul 20, 2018 · Artificial Intelligence

How Alibaba’s Brand‑Level Ranking Boosts E‑Commerce Clicks with Attention‑GRU

This article presents Alibaba’s first brand‑level ranking system that personalizes product ordering by modeling user brand preferences with an enhanced Attention‑GRU, detailing feature engineering, model improvements, extensive offline experiments on a massive Tmall dataset, and a successful online A/B test that increased CTR, ATIP, and GMV.

attention GRUbrand rankingdeep learning

0 likes · 27 min read

ITFLY8 Architecture Home

Jul 2, 2018 · Artificial Intelligence

How JD.com Built a Multi‑Screen Personalized Recommendation Engine

This article explains how JD.com evolved its recommendation system from simple product suggestions to a sophisticated, multi‑screen, multi‑type personalized engine using big‑data collection, real‑time behavior tracking, machine‑learning models, and a modular architecture that boosts conversion and user experience.

Big DataMachine Learninge-commerce

0 likes · 14 min read

How JD.com Built a Multi‑Screen Personalized Recommendation Engine

Architecture Digest

Jul 1, 2018 · Artificial Intelligence

Evolution and Architecture of JD.com Recommendation System

The article outlines the development, multi‑screen deployment, system architecture, data platform, and core recommendation engine of JD.com’s e‑commerce recommendation platform, highlighting how big‑data and AI techniques enable personalized product, activity, and content suggestions across various user touchpoints.

Machine Learninge-commercepersonalization

0 likes · 16 min read

Evolution and Architecture of JD.com Recommendation System

JD Retail Technology

Jun 20, 2018 · Artificial Intelligence

JD.com's 618 Shopping Festival: AI and Big Data Drive Seamless Retail Experience

JD.com's 618 shopping festival achieved 159.2 billion yuan in sales, leveraging AI and big data to enhance consumer experience and empower retail partners through intelligent supply chain solutions.

AIBig DataBlockchain

0 likes · 9 min read

JD.com's 618 Shopping Festival: AI and Big Data Drive Seamless Retail Experience

Java Backend Technology

Apr 20, 2018 · Artificial Intelligence

How Do Modern Recommendation Systems Balance Accuracy, Diversity, and Surprise?

This article explains the objectives, methods, architecture, and key algorithms of modern recommendation systems, covering popular, manual, related, and personalized approaches, the data pipeline, real‑time challenges, cold‑start handling, diversity, content quality, and exploration‑exploitation strategies.

Machine LearningReal-time ProcessingRecommendation Systems

0 likes · 15 min read

How Do Modern Recommendation Systems Balance Accuracy, Diversity, and Surprise?

21CTO

Apr 9, 2018 · Artificial Intelligence

How E‑Commerce Platforms Build Effective Product Recommendation Systems

This article explains the fundamentals and advanced techniques of e‑commerce product recommendation systems, covering conventional and personalized approaches, user profiling, data collection, storage, modeling, the three‑stage pipeline of preprocessing, recall and ranking, as well as system architecture, challenges, and key algorithms such as LR and GBDT.

Machine Learningdata pipelinee-commerce

0 likes · 17 min read

How E‑Commerce Platforms Build Effective Product Recommendation Systems

JD Tech

Mar 21, 2018 · Frontend Development

Dynamic Real‑Time Data Updates and Multi‑Scenario Personalization for Mini‑Program Frontends

This article describes how front‑end engineers solved dynamic real‑time operational data updates and multi‑scenario personalization for a mini‑program by leveraging a configuration system, web‑view integration, and lightweight JSON data structures, enabling rapid feature rollout with minimal backend effort.

Dynamic DataFrontendMini Program

0 likes · 7 min read

Dynamic Real‑Time Data Updates and Multi‑Scenario Personalization for Mini‑Program Frontends

Java Backend Technology

Feb 6, 2018 · Artificial Intelligence

How JD Built a Scalable AI-Powered Recommendation Engine for E‑Commerce

This article details JD's evolution from rule‑based recommendations to a multi‑screen, AI‑driven personalization platform, describing its system architecture, data pipelines, feature services, and key technologies that enable real‑time, user‑centric product suggestions across the e‑commerce ecosystem.

Artificial IntelligenceBig DataMachine Learning

0 likes · 20 min read

How JD Built a Scalable AI-Powered Recommendation Engine for E‑Commerce

Hujiang Design Center

Feb 1, 2018 · Fundamentals

What Are the 6 Interaction Design Trends Shaping 2018?

This article outlines six 2018 interaction design trends—including all‑sense experiences, screen‑less interfaces, emotionalized devices, natural voice interaction, AI‑driven personalization, cost‑effective interactions, and seamless online‑offline integration—backed by real‑world product examples and visual illustrations.

AIAR/VRInteraction Design

0 likes · 14 min read

What Are the 6 Interaction Design Trends Shaping 2018?

21CTO

Jan 18, 2018 · Artificial Intelligence

How Ctrip Scales Personalized Travel Recommendations: From Recall to Ranking

This article details Ctrip's end‑to‑end personalized recommendation system for travel, covering data collection, candidate recall methods, ranking models, feature engineering practices, and future directions, illustrating how millions of users receive tailored travel suggestions.

CtripMachine LearningRecommendation Systems

0 likes · 17 min read

How Ctrip Scales Personalized Travel Recommendations: From Recall to Ranking

Architecture Digest

Jan 18, 2018 · Artificial Intelligence

Personalized Recommendation System Practices at Ctrip: Data, Recall, Ranking, and Feature Engineering

The article details Ctrip's large‑scale personalized recommendation system, covering data sources, recall strategies, ranking models, feature engineering techniques, and practical insights from industry research to improve user experience in travel services.

CtripMachine LearningRecommendation Systems

0 likes · 16 min read

Personalized Recommendation System Practices at Ctrip: Data, Recall, Ranking, and Feature Engineering

Alibaba Cloud Developer

Jan 12, 2018 · Artificial Intelligence

How Alibaba’s New AI-Powered Ad Retrieval Model Redefined E‑Commerce Sponsored Search

Alibaba’s latest AI-driven ad retrieval framework, unveiled at WWW 2018, replaces keyword‑based search with a user‑behavior heterogeneous graph and machine‑learning models, delivering personalized, high‑efficiency ad matching that boosts ROI for advertisers, improves user experience, and enhances platform revenue.

Machine Learningad retrievale-commerce advertising

0 likes · 9 min read

How Alibaba’s New AI-Powered Ad Retrieval Model Redefined E‑Commerce Sponsored Search

Ctrip Technology

Jan 11, 2018 · Artificial Intelligence

Ctrip's Personalized Recommendation System: Data, Recall, and Ranking Practices

This article details Ctrip's large‑scale personalized recommendation system, covering the data sources, recall strategies, ranking models, feature engineering techniques, and future directions for improving recommendation quality in the travel domain.

deep learningpersonalizationranking

0 likes · 19 min read

Ctrip's Personalized Recommendation System: Data, Recall, and Ranking Practices

Alibaba Cloud Developer

Dec 29, 2017 · Artificial Intelligence

How Alibaba Leverages Deep Learning to Revolutionize E‑Commerce Search

Alibaba’s search team outlines how deep learning transforms e‑commerce search and recommendation, detailing system infrastructure, AI‑driven features like intelligent interaction, semantic search, personalized matching, performance optimizations, multi‑agent learning, and future plans for unified user and query representations.

AIdeep learninge-commerce

0 likes · 17 min read

How Alibaba Leverages Deep Learning to Revolutionize E‑Commerce Search

Meitu Technology

Dec 19, 2017 · Big Data

Meitu Internet Technology Salon Session 7: Practices in Recommendation Algorithms, Big Data, and Personalized Recommendation

At Meitu’s seventh Internet Technology Salon in Xiamen, over a hundred experts discussed recommendation algorithms and big‑data solutions, with talks on the Arachnia log‑collection system, the Naix distributed bitmap service, Meitu’s personalized recommendation pipeline challenges, and novel data‑missing‑theory models for improved performance.

Big Datadata collectiondistributed bitmap

0 likes · 8 min read

Meitu Internet Technology Salon Session 7: Practices in Recommendation Algorithms, Big Data, and Personalized Recommendation

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

Sep 15, 2017 · Artificial Intelligence

Mastering Recommendation Systems: Goals, Algorithms, and Real-World Practices

This article explains the objectives of recommendation systems, outlines four recommendation approaches, dives into personalized recommendation architecture and core algorithms, and discusses practical challenges such as real‑time processing, cold‑start, diversity, content quality, and exploration‑exploitation trade‑offs.

Recommendation Systemscold startcollaborative filtering

0 likes · 16 min read

Mastering Recommendation Systems: Goals, Algorithms, and Real-World Practices

Architecture Digest

Sep 15, 2017 · Artificial Intelligence

Overview of Recommendation Systems: Goals, Methods, Architecture, and Practical Considerations

This article explains the objectives of recommendation systems, compares popular recommendation approaches, details the components and algorithms of personalized recommendation pipelines, and discusses practical challenges such as real‑time processing, freshness, cold‑start, diversity, content quality, and surprise handling.

Machine Learningcold startdata pipeline

0 likes · 15 min read

Overview of Recommendation Systems: Goals, Methods, Architecture, and Practical Considerations

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 ProcessingSystem Architecture

0 likes · 8 min read

Java High-Performance Architecture

Aug 18, 2017 · Databases

How Couchbase Powers Personalization, Real‑Time Big Data, and Content Management

This article explains how Couchbase, a distributed NoSQL database, enables personalization, real‑time big‑data processing, and flexible content management for large enterprises, highlighting key requirements, solutions, and real‑world case studies from AOL, PayPal, and a Fortune‑500 media company.

CouchbaseDatabaseNoSQL

0 likes · 11 min read

How Couchbase Powers Personalization, Real‑Time Big Data, and Content Management

21CTO

Aug 17, 2017 · Artificial Intelligence

How Alibaba’s Deep Interest Network Powers Personalized Shopping for 400 Million Users

Alibaba’s Vice President Gu XueMei explained at the 40th ACM SIGIR conference how deep interest networks, driven by big data and large‑scale deep learning, enable highly personalized e‑commerce experiences that dramatically reduce user churn and boost click‑through rates.

Recommendation Systemse-commercepersonalization

0 likes · 5 min read

How Alibaba’s Deep Interest Network Powers Personalized Shopping for 400 Million Users

21CTO

Jun 20, 2017 · Artificial Intelligence

How Toutiao’s AI Powers Personalized News Recommendations

This article examines Toutiao’s rapid rise as a personalized news platform, detailing its AI‑driven recommendation pipeline, web‑crawling infrastructure, similarity‑matrix algorithms, A/B testing, and the role of human moderation in delivering highly targeted content to billions of users.

A/B testingAIBig Data

0 likes · 16 min read

How Toutiao’s AI Powers Personalized News Recommendations

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

Qunar Tech Salon

May 9, 2017 · Artificial Intelligence

Ctrip CTO Gan Quan on Building a Data‑Driven Personalized Recommendation System

The article details how Ctrip’s CTO Gan Quan has leveraged big‑data platforms, deep‑learning algorithms, cross‑screen user tracking, and rapid AB testing to create a real‑time, personalized recommendation engine that shortens travel decision cycles and drives significant revenue growth.

AB testingAIBig Data

0 likes · 14 min read

Ctrip CTO Gan Quan on Building a Data‑Driven Personalized Recommendation System

Ctrip Technology

May 8, 2017 · Artificial Intelligence

Ctrip CTO Gan Quan on Building a Data‑Driven Personalized Recommendation System

The article details Ctrip CTO Gan Quan’s insights on how the travel platform leverages a comprehensive big‑data infrastructure, AI‑driven algorithms, and real‑time user behavior tracking to deliver personalized travel recommendations, improve conversion rates, and shorten user decision cycles across multiple business lines.

AICtripTravel

0 likes · 18 min read

High Availability Architecture

May 4, 2017 · Artificial Intelligence

Quora’s 2017 Machine‑Learning Roadmap: Use Cases, Models, and Platform

The article presents a comprehensive 2017 overview of how Quora applies machine learning across question understanding, answer ranking, personalization, content quality, and ad optimization, detailing the models, libraries, and platform infrastructure that power these systems.

AIMachine Learningcontent moderation

0 likes · 12 min read

Quora’s 2017 Machine‑Learning Roadmap: Use Cases, Models, and Platform

Architecture Digest

Apr 2, 2017 · Artificial Intelligence

Mogujie's Search System Architecture and Online Request Flow

This article introduces Mogujie's end‑to‑end search system architecture, detailing its online and offline components such as Topn, ABTest, QR, fine‑ranking, search engine, UPS, and feature platforms, and then walks through a real‑world online request example to illustrate how queries are processed, rewritten, personalized, and finally ranked.

MogujieQuery RewriteSearch Architecture

0 likes · 11 min read

Mogujie's Search System Architecture and Online Request Flow

21CTO

Apr 1, 2017 · Artificial Intelligence

How Modern Apps Use AI to Personalize Your Content Feed

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

Machine LearningRecommendation Systemscontent filtering

0 likes · 7 min read

How Modern Apps Use AI to Personalize Your Content Feed

Alibaba Cloud Developer

Feb 22, 2017 · Artificial Intelligence

How Alibaba’s AI Powers Real‑Time Customer Segmentation and Personalized Shopping

This article explains how Alibaba leverages AI, big‑data analytics, and advanced recommendation algorithms to enable real‑time visitor clustering, personalized storefronts, and tailored content across its Customer Operation Platform, Double 11 promotion pages, QianNiu headlines, and service market, delivering significant conversion and engagement gains.

AIBig DataRecommendation Systems

0 likes · 18 min read

How Alibaba’s AI Powers Real‑Time Customer Segmentation and Personalized Shopping

Alibaba Cloud Developer

Feb 20, 2017 · Artificial Intelligence

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

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

Machine Learninge-commercegraph embedding

0 likes · 13 min read

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

Alibaba Cloud Developer

Jan 11, 2017 · R&D Management

How Taobao’s Beehive Platform Powers Content‑Driven Shopping During Double 11

The article explains how Taobao’s content‑centric strategy, embodied in the Beehive platform, builds an end‑to‑end content chain—from creator tools and health scoring to personalized distribution and commerce mechanisms—enabling massive, efficient content production and monetization during the Double 11 shopping festival.

Big DataTaobaocontent platform

0 likes · 17 min read

How Taobao’s Beehive Platform Powers Content‑Driven Shopping During Double 11

Hulu Beijing

Dec 27, 2016 · Artificial Intelligence

Inside Hulu’s AI Research: Personalization, Data Science & Video Innovation

The article announces a PhD workshop, outlines Hulu’s research center and its six AI‑focused teams—personalization, data science, video codec, content understanding, intelligent search, and ad intelligence—while highlighting key projects and inviting PhD candidates to apply.

AIAd TechIntelligent Search

0 likes · 7 min read

Inside Hulu’s AI Research: Personalization, Data Science & Video Innovation

Alibaba Cloud Developer

Dec 8, 2016 · Artificial Intelligence

How AI Powers Data‑Driven Merchant Success

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

AIAlibabaBig Data

0 likes · 2 min read

How AI Powers Data‑Driven Merchant Success

21CTO

Oct 21, 2016 · Artificial Intelligence

How Toutiao Dominated Chinese News with AI‑Powered Personalization

This article examines Toutiao’s evolution from a simple news aggregator to a 600‑billion‑RMB valued AI‑driven recommendation platform, detailing its market growth, data‑driven personalization, product features, business model, talent philosophy, and future outlook.

AIBig DataRecommendation Engine

0 likes · 10 min read

How Toutiao Dominated Chinese News with AI‑Powered Personalization

StarRing Big Data Open Lab

Oct 13, 2016 · Artificial Intelligence

Why Data Mining Matters: A Programmer’s Guide to Building Recommendation Systems

This article introduces a programmer‑focused guide to data mining, explaining how personalized recommendation systems work, illustrating historical and modern examples, and outlining the book’s practical Python‑based approach to mastering big‑scale data analysis.

Big DataMachine LearningPython

0 likes · 19 min read

Why Data Mining Matters: A Programmer’s Guide to Building Recommendation Systems

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

How User Profiling Powers Modern Recommendation Systems

Qunar Tech Salon

Aug 20, 2016 · Artificial Intelligence

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

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

Machine LearningQunarcollaborative filtering

0 likes · 14 min read

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

21CTO

Aug 6, 2016 · Artificial Intelligence

How Toutiao Scales Personalized News: Architecture and Recommendation Engine

This article outlines Toutiao's rapid growth, its data pipelines, user modeling, recommendation system, storage solutions, and push notification strategies that together enable personalized news delivery to hundreds of millions of users.

AIBig DataToutiao

0 likes · 7 min read

How Toutiao Scales Personalized News: Architecture and Recommendation Engine

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

Mar 18, 2016 · Artificial Intelligence

10 Essential Tips for Building High‑Performance Intelligent Recommendation Systems

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

Machine Learningcold startdata mining

0 likes · 18 min read

10 Essential Tips for Building High‑Performance Intelligent Recommendation Systems

Architecture Digest

Mar 14, 2016 · Mobile Development

Optimizing Mobile Taobao Main Venue Performance and Personalization with a Reusable Framework and Image‑Merging System

The article details how Alibaba's Mobile Taobao team redesigned the main promotional venue using a reusable native framework, dynamic Weex rendering, cloud‑driven configuration, pre‑heat data push, multi‑strategy routing, and a large‑scale image‑merging system to improve browsing speed, reduce bounce rates, and support flexible, personalized large‑scale sales events.

Performance Optimizationframework designimage processing

0 likes · 15 min read

Optimizing Mobile Taobao Main Venue Performance and Personalization with a Reusable Framework and Image‑Merging System

Qunar Tech Salon

Feb 24, 2016 · Artificial Intelligence

Overview and Architecture of Pora: A Real‑Time Personalization Analytics Platform

The article introduces Pora, a real‑time offline‑realtime analytics system for personalized search that combines high‑throughput stream processing, low‑latency computation, online learning algorithms, and a modular architecture to support continuous 24/7 operation and large‑scale performance optimizations.

AIOnline LearningSearch

0 likes · 6 min read

Overview and Architecture of Pora: A Real‑Time Personalization Analytics Platform

21CTO

Feb 17, 2016 · Big Data

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

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

Machine Learninge-commercepersonalization

0 likes · 11 min read

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

21CTO

Jan 25, 2016 · Big Data

How Alibaba’s Pora Powers Real‑Time Personalization at Massive Scale

Pora (Personal Offline Realtime Analyze) is a high‑throughput, low‑latency platform that captures user behavior in real time, enabling Alibaba’s search engine to deliver personalized results, support online learning, and run 24/7 with massive data volumes.

AlibabaBig DataPora

0 likes · 6 min read

How Alibaba’s Pora Powers Real‑Time Personalization at Massive Scale

21CTO

Jan 11, 2016 · Artificial Intelligence

How WeChat Serves Tailored Ads: Inside the Recommendation Algorithm

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

Machine LearningWeChat advertisingcontent-based filtering

0 likes · 5 min read

How WeChat Serves Tailored Ads: Inside the Recommendation Algorithm

21CTO

Jan 3, 2016 · Artificial Intelligence

How Meilishuo Personalizes Fashion: Inside Its AI‑Driven Recommendation Engine

This article explores how Meilishuo, China’s leading fast‑fashion discovery platform, tackles fragmented mobile attention by using AI‑powered personalization techniques—including user modeling, real‑time feedback, and tailored push notifications—to deliver highly relevant fashion recommendations and boost user engagement.

AIe-commercepersonalization

0 likes · 6 min read

How Meilishuo Personalizes Fashion: Inside Its AI‑Driven Recommendation Engine

Architects Research Society

Dec 26, 2015 · Artificial Intelligence

JD.com’s Personalized Recommendation System: Architecture, Models, and Future Directions

The article explains how JD.com leverages big‑data and personalized recommendation algorithms across PC and mobile platforms, detailing its recall and ranking models, efficiency analysis, weekly algorithm iterations, and future AI‑driven optimizations that together contribute about 10% of its orders.

JD.come-commercepersonalization

0 likes · 10 min read

JD.com’s Personalized Recommendation System: Architecture, Models, and Future Directions

Architects Research Society

Dec 12, 2015 · Artificial Intelligence

Personalized Recommendation Best Practices

This article explains the fundamentals and business value of personalized recommendation systems for e‑commerce, outlines practical implementations on homepages, list pages, and search result pages, and provides case studies showing how tailored product suggestions improve conversion rates, user experience, and sales performance.

AIRecommendation Systemse-commerce

0 likes · 11 min read

Personalized Recommendation Best Practices

21CTO

Dec 3, 2015 · Artificial Intelligence

How Baidu Mobile Personalizes Content: Insights into Recommendation Systems and Cloud Architecture

This article explores Baidu Mobile's approach to personalization, detailing its recommendation algorithms, cloud‑side architecture, and front‑end engineering practices that together enable a highly tailored user experience across billions of users.

Artificial Intelligencecloud architecturefrontend development

0 likes · 14 min read

How Baidu Mobile Personalizes Content: Insights into Recommendation Systems and Cloud Architecture

21CTO

Nov 18, 2015 · Artificial Intelligence

Inside Baidu Mobile’s Personalization: Recommendation Engine & Cloud Architecture

This article examines how Baidu Mobile leverages personalized recommendation algorithms, rich user profiling, and a flexible cloud‑native architecture to deliver tailored search results and services, while also detailing the front‑end engineering practices that support its super‑app ecosystem.

Frontendbackendcloud architecture

0 likes · 15 min read

Inside Baidu Mobile’s Personalization: Recommendation Engine & Cloud Architecture

21CTO

Sep 1, 2015 · Artificial Intelligence

How the NYT Revamped Its Recommendation Engine with Collaborative Topic Modeling

This article explains how the New York Times redesigned its "Recommended for You" system by combining content‑based filtering, collaborative filtering, and a collaborative topic‑modeling approach that uses LDA, reader‑signal adjustments, and fast preference calculations to deliver personalized article suggestions.

LDARecommendation Systemscollaborative filtering

0 likes · 12 min read

How the NYT Revamped Its Recommendation Engine with Collaborative Topic Modeling

21CTO

Aug 21, 2015 · Artificial Intelligence

How Quora Leverages Machine Learning for Ranking, Personalization, and Moderation

Quora employs a variety of machine‑learning techniques—from ranking and personalized feed algorithms to duplicate‑question detection, user expertise inference, and content moderation—optimizing both user experience and content quality through offline testing, online A/B experiments, and models such as logistic regression, gradient‑boosted trees, and neural networks.

moderationpersonalizationquora

0 likes · 11 min read

How Quora Leverages Machine Learning for Ranking, Personalization, and Moderation

Meituan Technology Team

Jan 31, 2015 · Artificial Intelligence

Meituan Recommendation System Architecture and Optimization Practices

Meituan’s recommendation platform comprises a data layer, a multi‑strategy candidate generation layer, a fusion‑and‑filtering layer, and a ranking layer that uses additive‑grove tree ensembles and online‑updated logistic regression, leveraging extensive user behavior logs, location, query, graph and real‑time signals to deliver personalized deals.

Machine LearningMeituanpersonalization

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Meituan Recommendation System Architecture and Optimization Practices