BestHub
Sign in
Tagged articles

personalization

172 articles · Page 2 of 2
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 7, 2019 · Artificial Intelligence

How KOBE Transforms Personalized Recommendation Reason Generation with Transformers

This article introduces KOBE, a knowledge‑based personalized text generation system that leverages Transformer architecture, attribute fusion, and external knowledge graphs to produce fluent, domain‑aware recommendation reasons for e‑commerce products, with a case study on the Spring Festival cloud theme.

Knowledge GraphTransformerpersonalization
0 likes · 13 min read
How KOBE Transforms Personalized Recommendation Reason Generation with Transformers
Suning Technology
Suning Technology
Aug 2, 2019 · Big Data

How SuNing Uses Big Data to Revolutionize Retail Supply Chains

At the 15th China (Nanjing) International Software Expo, SuNing's VP shared how the company applies big‑data analytics, the C2M model, and flexible manufacturing to personalize retail experiences, bridge online‑offline gaps, and drive data‑driven product development and supply‑chain efficiency.

Big DataC2Mdata-driven
0 likes · 9 min read
How SuNing Uses Big Data to Revolutionize Retail Supply Chains
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.”

AIBig DataJD.com
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
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.

AlgorithmsRankingdata
0 likes · 25 min read
From the Pre‑Recommendation Era to the Bronze Age: Evolution of Recommendation Systems and Mitigating the Matthew Effect
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
NetEase Media Technology Team
NetEase Media Technology Team
Jul 2, 2019 · Backend Development

Design and Implementation of Feed Stream Architecture for NetEase Open Courses

The article details NetEase Open Courses’ feed‑stream architecture, describing how content ingestion, multi‑level filtering, vertically and horizontally split storage, Elasticsearch indexing, two‑tier caching, and micro‑service orchestration combine to deliver personalized, high‑availability course feeds while addressing scalability, consistency, and operational challenges.

Backend ArchitectureData Storagecaching
0 likes · 16 min read
Design and Implementation of Feed Stream Architecture for NetEase Open Courses
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
JD Retail Technology
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.

AIRecommendation Systemsalgorithm optimization
0 likes · 6 min read
Comprehensive 6.18 Preparation: Load Testing, Deep Personalization, and Recommendation Algorithm Optimizations
DataFunTalk
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
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 DataCollaborative Filteringmachine learning
0 likes · 18 min read
Practical Implementation of Personalized Recommendation Systems: Overview, Algorithms, Challenges, and Architecture
Sohu Tech Products
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
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
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.

AICollaborative FilteringReal-time feedback
0 likes · 17 min read
Evolution and Architecture of Beike's Intelligent Real Estate Recommendation Platform
58UXD
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
How 58.com Reimagined Its Homepage: From User‑Centric Design to a Youthful Brand Experience
DataFunTalk
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.

AIData Engineeringfinance
0 likes · 14 min read
Personalized Recommendation System of 51 Credit Card: Architecture, Challenges, and Growth Cases
21CTO
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 testingRecommendation Systemsalgorithm design
0 likes · 10 min read
Why Recommendation Algorithms Aren’t Magic: A Practical Guide
Xianyu Technology
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
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
How Alibaba’s Brand‑Level Ranking Boosts E‑Commerce Clicks with Attention‑GRU
ITFLY8 Architecture Home
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 Datae-commercemachine learning
0 likes · 14 min read
How JD.com Built a Multi‑Screen Personalized Recommendation Engine
Architecture Digest
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.

e-commercemachine learningpersonalization
0 likes · 16 min read
Evolution and Architecture of JD.com Recommendation System
Java Backend Technology
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.

Collaborative FilteringRecommendation Systemscontent-based filtering
0 likes · 15 min read
How Do Modern Recommendation Systems Balance Accuracy, Diversity, and Surprise?
21CTO
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.

Data Pipelinee-commercemachine learning
0 likes · 17 min read
How E‑Commerce Platforms Build Effective Product Recommendation Systems
JD Tech
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.

ConfigurationDynamic DataFrontend
0 likes · 7 min read
Dynamic Real‑Time Data Updates and Multi‑Scenario Personalization for Mini‑Program Frontends
Java Backend Technology
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 Datae-commerce
0 likes · 20 min read
How JD Built a Scalable AI-Powered Recommendation Engine for E‑Commerce
Hujiang Design Center
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/VRdesign trends
0 likes · 14 min read
What Are the 6 Interaction Design Trends Shaping 2018?
21CTO
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.

CtripRecommendation SystemsTravel
0 likes · 17 min read
How Ctrip Scales Personalized Travel Recommendations: From Recall to Ranking
Alibaba Cloud Developer
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.

ad retrievale-commerce advertisingheterogeneous graph
0 likes · 9 min read
How Alibaba’s New AI-Powered Ad Retrieval Model Redefined E‑Commerce Sponsored Search
Alibaba Cloud Developer
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.

AISearch Optimizationdeep learning
0 likes · 17 min read
How Alibaba Leverages Deep Learning to Revolutionize E‑Commerce Search
Meitu Technology
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
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 Systemsdata miningpersonalization
0 likes · 13 min read
How Tagging and User Profiling Power Modern Recommendation Systems
21CTO
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.

Collaborative FilteringRecommendation Systemscold start
0 likes · 16 min read
Mastering Recommendation Systems: Goals, Algorithms, and Real-World Practices
Architecture Digest
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.

Data Pipelinecold startevaluation
0 likes · 15 min read
Overview of Recommendation Systems: Goals, Methods, Architecture, and Practical Considerations
21CTO
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.

CtripData Pipelinepersonalization
0 likes · 8 min read
How Ctrip Builds a Scalable User Profile Platform for Personalized Travel
21CTO
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
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
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
Ctrip Technology
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
Ctrip CTO Gan Quan on Building a Data‑Driven Personalized Recommendation System
Architecture Digest
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 RewriteRanking
0 likes · 11 min read
Mogujie's Search System Architecture and Online Request Flow
21CTO
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.

Recommendation Systemscontent filteringmachine learning
0 likes · 7 min read
How Modern Apps Use AI to Personalize Your Content Feed
Alibaba Cloud Developer
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
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.

e-commercegraph embeddingmachine learning
0 likes · 13 min read
How Alibaba’s Graph Embedding Boosts E‑Commerce Recommendations by 60%
Alibaba Cloud Developer
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
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
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
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
21CTO
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
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.

Collaborative FilteringQunarRanking
0 likes · 14 min read
Personalized Demand Prediction and Ranking for Qunar App’s “You May Like” Card
Baidu Intelligent Testing
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
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.

User Behaviorcold startdata mining
0 likes · 18 min read
10 Essential Tips for Building High‑Performance Intelligent Recommendation Systems
Architecture Digest
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.

Framework Designimage processingperformance optimization
0 likes · 15 min read
Optimizing Mobile Taobao Main Venue Performance and Personalization with a Reusable Framework and Image‑Merging System
Qunar Tech Salon
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.

AIReal-Time AnalyticsStream Processing
0 likes · 6 min read
Overview and Architecture of Pora: A Real‑Time Personalization Analytics Platform
21CTO
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.

e-commercemachine learningpersonalization
0 likes · 11 min read
How Big Data Powers Personalized Recommendations in Mother‑Baby E‑Commerce
21CTO
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
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.

WeChat advertisingcontent-based filteringmachine learning
0 likes · 5 min read
How WeChat Serves Tailored Ads: Inside the Recommendation Algorithm
21CTO
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.

AIUser Modelinge-commerce
0 likes · 6 min read
How Meilishuo Personalizes Fashion: Inside Its AI‑Driven Recommendation Engine
Architects Research Society
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
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
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
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.

Collaborative FilteringLDARecommendation Systems
0 likes · 12 min read
How the NYT Revamped Its Recommendation Engine with Collaborative Topic Modeling
21CTO
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.

Rankingmoderationpersonalization
0 likes · 11 min read
How Quora Leverages Machine Learning for Ranking, Personalization, and Moderation
Meituan Technology Team
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.

MeituanRankingmachine learning
0 likes · 14 min read
Meituan Recommendation System Architecture and Optimization Practices