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Alibaba Cloud Developer
Alibaba Cloud Developer
May 7, 2020 · Artificial Intelligence

How Hierarchical Attention Boosts Dynamic Heterogeneous Graph Embedding

This article introduces DyHAN, a hierarchical attention‑based dynamic heterogeneous graph embedding method that captures temporal, node‑level, and edge‑level information, demonstrates superior offline and online performance on Alibaba’s ICBU recommendation system, and discusses dataset construction, model architecture, and future challenges.

DyHANHierarchical Attentiondynamic heterogeneous graph
0 likes · 10 min read
How Hierarchical Attention Boosts Dynamic Heterogeneous Graph Embedding
DataFunTalk
DataFunTalk
Apr 24, 2020 · Artificial Intelligence

Common Pitfalls in Recommendation Systems: Metrics, Exploration‑Exploitation, and Offline‑Online Discrepancies

The article surveys typical challenges in recommendation systems, including ambiguous evaluation metrics, the trade‑off between precise algorithms and user experience, the exploration‑exploitation dilemma, and why offline AUC improvements often lead to online CTR/CPM drops due to data leakage, feature inconsistency, and distribution shifts.

AUCCTRExploration-Exploitation
0 likes · 14 min read
Common Pitfalls in Recommendation Systems: Metrics, Exploration‑Exploitation, and Offline‑Online Discrepancies
ITPUB
ITPUB
Apr 12, 2020 · Big Data

Inside Toutiao’s Massive Data Pipeline and Real‑Time Recommendation Engine

This article details how Toutiao processes billions of daily page views, builds user models with Hadoop and Storm, runs real‑time recommendation and cold‑start personalization, and scales its microservice‑based architecture using Kafka, MySQL, MongoDB, Redis and a high‑throughput push system.

data pipelinerecommendation system
0 likes · 10 min read
Inside Toutiao’s Massive Data Pipeline and Real‑Time Recommendation Engine
JD Retail Technology
JD Retail Technology
Apr 7, 2020 · Artificial Intelligence

Fine-Grained Personalized Recommendation System Architecture for E-commerce

This article outlines the engineering architecture of a fine‑grained, personalized recommendation system for e‑commerce, covering core components such as feature data (offline and real‑time), algorithm engine (recall and ranking), technology choices like MongoDB, Elasticsearch, Kafka, Redis, and model deployment strategies.

algorithm enginee‑commercefeature data
0 likes · 9 min read
Fine-Grained Personalized Recommendation System Architecture for E-commerce
58 Tech
58 Tech
Apr 1, 2020 · Artificial Intelligence

Intelligent Recommendation System for 58 Tongzhen: Architecture, Data, Features, and Model Evolution

This article describes how 58 Tongzhen leverages AI technologies—including data pipelines, feature engineering, various recall and ranking models, and AB‑testing—to build a personalized feed recommendation system for the down‑market, detailing its overall architecture, data sources, model iterations, performance gains, and future directions.

AB testingAIDeep Learning
0 likes · 20 min read
Intelligent Recommendation System for 58 Tongzhen: Architecture, Data, Features, and Model Evolution
DataFunTalk
DataFunTalk
Mar 31, 2020 · Artificial Intelligence

Design and Evolution of the Quality Control Framework for WeChat Look Feature

This article presents the overall design, multi‑dimensional control mechanisms, auxiliary modules, and evolutionary processes of the quality control system used in WeChat's Look feature, detailing data lifecycle, model training, generalization, transfer learning, and continuous anti‑abuse strategies.

Model Trainingcontent moderationmachine learning
0 likes · 18 min read
Design and Evolution of the Quality Control Framework for WeChat Look Feature
DataFunTalk
DataFunTalk
Mar 26, 2020 · Artificial Intelligence

Building a Personalized Live‑Streaming Recommendation System: From Basics to Advanced Models at Huajiao Live

This article explains how Huajiao Live designed and evolved its live‑streaming recommendation system, covering basic concepts, collaborative‑filtering and matrix‑factorization techniques, deep‑learning models, ranking and multi‑objective optimization, and practical deployment considerations for real‑time personalized content delivery.

Deep LearningHuajiaocollaborative filtering
0 likes · 16 min read
Building a Personalized Live‑Streaming Recommendation System: From Basics to Advanced Models at Huajiao Live
Huajiao Technology
Huajiao Technology
Mar 24, 2020 · Artificial Intelligence

How to Overcome Recommendation Cold Start: Methods and Huajiao Live’s Real‑World Practices

This article explains the cold‑start problem in recommendation systems, outlines common industry solutions such as popular‑content, group‑representative, auxiliary‑information, bandit algorithms, and deep learning, and details how Huajiao Live applied these techniques to improve new‑user engagement and metrics.

Deep LearningHuajiao Livebandit algorithm
0 likes · 13 min read
How to Overcome Recommendation Cold Start: Methods and Huajiao Live’s Real‑World Practices
DataFunTalk
DataFunTalk
Mar 23, 2020 · Artificial Intelligence

Deep Learning Applications in Alibaba 1688 B2B E‑commerce Recommendation System: From Deep Match to Live Content Ranking

This article details how Alibaba's 1688 B2B platform leverages deep learning techniques—including Deep Match, DIN, DIEN, DMR, and heterogeneous network models—to evolve its product recall, ranking, and live‑content recommendation pipelines, highlighting system architecture, practical lessons, and online performance improvements.

AlibabaDeep Learninge‑commerce
0 likes · 14 min read
Deep Learning Applications in Alibaba 1688 B2B E‑commerce Recommendation System: From Deep Match to Live Content Ranking
DataFunTalk
DataFunTalk
Mar 18, 2020 · Artificial Intelligence

Personalized Push Notification System: Embedding, Recall, and Ranking Techniques at Meitu

This article presents a comprehensive technical overview of Meitu's personalized push notification pipeline, detailing the evolution of embedding methods (Word2Vec, Airbnb listing embedding, graph embedding), multiple recall strategies (global, personalized, attribute, and content‑based), and a progression of ranking models from logistic regression to field‑wise three‑tower architectures, highlighting their impact on click‑through rates.

AIDeep LearningPush Notification
0 likes · 12 min read
Personalized Push Notification System: Embedding, Recall, and Ranking Techniques at Meitu
Full-Stack Internet Architecture
Full-Stack Internet Architecture
Mar 17, 2020 · Fundamentals

A Real‑Life Example of User Profiling to Boost Sales

This article uses a vivid kite‑selling story to illustrate how user profiling, data tagging, and recommendation tactics can be combined to increase transaction volume, improve average order value, and avoid common pitfalls such as unclear goals, poor data quality, and unvalidated tags.

Data QualityMarketing Strategydata analysis
0 likes · 9 min read
A Real‑Life Example of User Profiling to Boost Sales
DataFunTalk
DataFunTalk
Mar 16, 2020 · Artificial Intelligence

Phoenix News Feed Recommendation System: Architecture, Modeling, and Feature Engineering

This article presents a comprehensive overview of Phoenix News's AI‑driven feed recommendation system, detailing its business challenges, multi‑stage architecture, deep learning models, feature pipelines, metric trade‑offs, cold‑start solutions, and practical insights for improving user satisfaction and content quality.

AIDeep Learningfeature engineering
0 likes · 22 min read
Phoenix News Feed Recommendation System: Architecture, Modeling, and Feature Engineering
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 13, 2020 · Artificial Intelligence

How Deep Match to Rank Boosts CTR Prediction in E‑Commerce Recommendations

The article presents the Deep Match to Rank (DMR) model, which integrates collaborative‑filtering inspired user‑to‑item relevance modeling into the ranking stage of recommendation systems, achieving significant offline and online improvements in click‑through rate and revenue metrics for e‑commerce platforms.

CTR predictionDeep Learninge‑commerce
0 likes · 11 min read
How Deep Match to Rank Boosts CTR Prediction in E‑Commerce Recommendations
Qunar Tech Salon
Qunar Tech Salon
Mar 13, 2020 · Artificial Intelligence

The Evolution of AutoHome Recommendation System Ranking Algorithms

This article details the architecture, model evolution, feature processing, online learning, and future optimization plans of AutoHome's recommendation system, covering stages from resource collection to ranking, various models such as LR, XGBoost, FM, DeepFM, and operational practices like AB testing and debugging.

Online Learningfeature engineeringranking algorithm
0 likes · 18 min read
The Evolution of AutoHome Recommendation System Ranking Algorithms
DataFunTalk
DataFunTalk
Mar 12, 2020 · Artificial Intelligence

Model Evolution and Optimization for Recommendation Systems in a Mid‑size E‑commerce App

This article describes the end‑to‑end recommendation pipeline of the Province Money Fast Report app, covering business background, data collection, model training and evaluation, the evolution from FM to DeepFM, DIN, DCN, xDeepFM, ESMM and custom networks, as well as serving strategies and practical lessons learned.

CTR predictionDeep LearningModel Serving
0 likes · 28 min read
Model Evolution and Optimization for Recommendation Systems in a Mid‑size E‑commerce App
DataFunTalk
DataFunTalk
Mar 4, 2020 · Artificial Intelligence

Building and Applying Relationship Graphs at Beike Real Estate: Architecture, Embedding, and Recommendation

The talk explains how Beike Real Estate constructs a large‑scale relationship graph from billions of user, house, and agent interactions, quantifies edge strengths, builds homogeneous and heterogeneous sub‑graphs, derives graph capabilities such as node influence, embedding, similarity and relation prediction, and finally deploys these capabilities in multi‑degree queries, house‑similarity recommendations and B‑side agent assistance, achieving measurable CTR improvements.

AIKnowledge Graphgraph embedding
0 likes · 17 min read
Building and Applying Relationship Graphs at Beike Real Estate: Architecture, Embedding, and Recommendation
21CTO
21CTO
Feb 18, 2020 · Artificial Intelligence

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

This article details Toutiao’s large‑scale recommendation system, explaining how it models content, user, and environment features, the variety of algorithms and real‑time training pipelines used, feature engineering categories, recall strategies, content analysis, user tagging, evaluation methods, and content‑safety mechanisms.

Content SafetyReal-time Trainingevaluation
0 likes · 18 min read
Inside Toutiao’s Real‑Time Recommendation Engine: Architecture, Features, and Evaluation
Big Data Technology & Architecture
Big Data Technology & Architecture
Feb 5, 2020 · Big Data

2019 Sensor Data Whitepapers: Recommendation Systems, Tag Profiling, Data‑Research‑Product Integration, Event Tracking, and Big Data Architecture

This article lists several 2019 whitepapers from Sensor Data covering how enterprises can build recommendation systems from scratch, construct tag‑profile frameworks, integrate data research with product development, implement enterprise event‑tracking methodologies, and understand big‑data technologies and architectures.

event trackingrecommendation systemtag profiling
0 likes · 3 min read
2019 Sensor Data Whitepapers: Recommendation Systems, Tag Profiling, Data‑Research‑Product Integration, Event Tracking, and Big Data Architecture
HomeTech
HomeTech
Jan 15, 2020 · Artificial Intelligence

Architecture and Components of an Intelligent Recommendation Platform

The article outlines a micro‑service based intelligent recommendation platform that supports over 40 scenarios, detailing its overall architecture, AB testing service, and the three core modules—index, recall, and filter—while also describing future plans for platform centralization and open development.

AB testingAIEngine Architecture
0 likes · 5 min read
Architecture and Components of an Intelligent Recommendation Platform
Alibaba Cloud Developer
Alibaba Cloud Developer
Jan 9, 2020 · Artificial Intelligence

How Edge Computing Transforms Real-Time Recommendation Systems

This article examines the limitations of cloud‑based recommendation pipelines, explains how edge computing can provide localized user perception and rapid re‑ranking, describes the EdgeRec on‑device model architecture—including heterogeneous behavior sequence modeling and behavior‑aware attention reranking—and presents offline and online experimental results that demonstrate significant gains in click‑through and conversion rates.

behavior modelingon-device AIreal-time personalization
0 likes · 16 min read
How Edge Computing Transforms Real-Time Recommendation Systems
Tencent Cloud Developer
Tencent Cloud Developer
Dec 3, 2019 · Artificial Intelligence

Feature Engineering Practices for Short‑Video Recommendation Systems

Effective short‑video recommendation relies on meticulous feature engineering that transforms raw signals—numerical counts, categorical IDs, content and user embeddings, context and session data—through bucketization, scaling, crossing, and smoothing, then selects and evaluates them via filtering, wrapping, regularization, and importance analysis to mitigate business biases and improve multi‑objective ranking performance.

Embeddingbias mitigationdata preprocessing
0 likes · 32 min read
Feature Engineering Practices for Short‑Video Recommendation Systems
dbaplus Community
dbaplus Community
Nov 18, 2019 · Backend Development

Designing an Off‑Heap Disaster Recovery Cache to Keep Recommendations Fast

When the recommendation service of the Mafengwo app experiences database disconnections, third‑party timeouts, or network jitter, a locally‑deployed off‑heap cache built with OHC and SpringBoot can return pre‑computed results, isolating business logic, reducing latency, and improving user experience during failures.

JavaOff-HeapSpringBoot
0 likes · 12 min read
Designing an Off‑Heap Disaster Recovery Cache to Keep Recommendations Fast
DataFunTalk
DataFunTalk
Nov 15, 2019 · Artificial Intelligence

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

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

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

Boosting Online Shopping with AI-Powered 3D Scene Merchandising

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

3d-visualizationDeep Learninge‑commerce
0 likes · 12 min read
Boosting Online Shopping with AI-Powered 3D Scene Merchandising
Mafengwo Technology
Mafengwo Technology
Nov 7, 2019 · Artificial Intelligence

Inside MaFengWo’s Scalable Ranking Platform: Architecture, Verification & Explainability

This article explains how MaFengWo’s recommendation system combines recall, ranking, and rerank stages, details the evolution of its sorting algorithm platform, and shows how data verification and model‑explainability techniques like SHAP and LIME improve online performance and accelerate model iteration.

Data verificationModel ExplainabilityXGBoost
0 likes · 13 min read
Inside MaFengWo’s Scalable Ranking Platform: Architecture, Verification & Explainability
JD Retail Technology
JD Retail Technology
Nov 6, 2019 · Artificial Intelligence

Technical Overview of JD.com Search and Recommendation Systems for the 11.11 Shopping Festival

The article details JD.com's internally developed distributed search engine and recommendation platform, their new architectures, deep‑learning‑driven ranking and recall models, component‑based deployment, extensive performance testing, and coordinated operations that powered the massive 11.11 shopping event.

Deep LearningOperationsPerformance Testing
0 likes · 5 min read
Technical Overview of JD.com Search and Recommendation Systems for the 11.11 Shopping Festival
DataFunTalk
DataFunTalk
Nov 4, 2019 · Artificial Intelligence

Standardizing Model Training and Feature Processing in Recommendation Systems

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

CTR predictionModel Trainingfeature engineering
0 likes · 14 min read
Standardizing Model Training and Feature Processing in Recommendation Systems
iQIYI Technical Product Team
iQIYI Technical Product Team
Oct 31, 2019 · Artificial Intelligence

Online Learning for Large‑Scale DNN Ranking Models in iQIYI Feed Recommendation

iQIYI’s feed recommendation system adopts an online‑learning framework that continuously trains a massive Wide‑and‑Deep DNN on billions of streaming samples, handling dynamic user interests, OOV embeddings, delayed labels, and non‑convex optimization, enabling hourly model refreshes and delivering up to 3.8 % higher consumption versus offline baselines.

DNNOnline LearningReal-time Training
0 likes · 17 min read
Online Learning for Large‑Scale DNN Ranking Models in iQIYI Feed Recommendation
Snowball Engineer Team
Snowball Engineer Team
Oct 17, 2019 · Artificial Intelligence

GPU-Accelerated Model Training Optimizations for Snowball Feed Recommendation System

This article describes the challenges of large‑scale model training for Snowball’s feed recommendation, and details a series of engineering optimizations—including GPU acceleration, multi‑threaded data preparation, TFRecord conversion, compression, and batch‑map reordering—that increased training throughput from 6 k to over 20 k samples per second while reducing CPU and I/O bottlenecks.

GPUModel TrainingTFRecord
0 likes · 15 min read
GPU-Accelerated Model Training Optimizations for Snowball Feed Recommendation System
DataFunTalk
DataFunTalk
Oct 14, 2019 · Artificial Intelligence

Advances in Short Video Recommendation: Multi‑Objective Optimization and Model Enhancements

This article presents a comprehensive overview of short‑video recommendation at UC, covering business background, system architecture, the evolution from LR to Wide & Deep models, multi‑objective loss design with positive‑sample weighting, graph‑embedding fusion, time‑weighted loss, continuity modeling, a Boosting‑based WnD solution, and future research directions.

Deep Learningboostinggraph embedding
0 likes · 11 min read
Advances in Short Video Recommendation: Multi‑Objective Optimization and Model Enhancements
Alibaba Cloud Developer
Alibaba Cloud Developer
Oct 10, 2019 · Artificial Intelligence

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

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

A/B testinge‑commercemachine learning
0 likes · 12 min read
Boosting Spring Festival Activity: Alibaba’s Full‑Link Intelligent Delivery Framework
21CTO
21CTO
Oct 6, 2019 · Artificial Intelligence

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

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

Real-time Trainingcontent analysismachine learning
0 likes · 18 min read
How Toutiao’s AI Recommendation Engine Works: From Content Analysis to Real‑Time Ranking
DataFunTalk
DataFunTalk
Sep 12, 2019 · Artificial Intelligence

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

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

A/B testingCTR predictionSystem Architecture
0 likes · 14 min read
Exploring Personalized Recommendation at Kuaikan Comics: Business, Algorithms, and System Architecture
Architecture Digest
Architecture Digest
Sep 9, 2019 · Artificial Intelligence

Overview of Recommendation System Architecture, Algorithms, and Evaluation

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

A/B testingcollaborative filteringfeature engineering
0 likes · 28 min read
Overview of Recommendation System Architecture, Algorithms, and Evaluation
FangDuoduo UEDC
FangDuoduo UEDC
Sep 3, 2019 · Product Management

How Taobao’s Homepage Recommendations Work: Full‑Page vs Section‑Focused Browsing

This article examines Taobao’s homepage recommendation system, comparing the full‑page browsing mode that displays a continuous stream of items with the section‑focused mode that groups products into distinct blocks, highlighting their design principles, user experience implications, and underlying recommendation strategies.

TaobaoUI designUser experience
0 likes · 1 min read
How Taobao’s Homepage Recommendations Work: Full‑Page vs Section‑Focused Browsing
Tencent Cloud Developer
Tencent Cloud Developer
Aug 23, 2019 · Artificial Intelligence

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

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

content-based filteringmachine learningrecommendation system
0 likes · 13 min read
WeChat Reading "Guess You Like" Recommendation System: Algorithms and Architecture
Tencent Cloud Developer
Tencent Cloud Developer
Aug 9, 2019 · Artificial Intelligence

Real-time Attention-based Look-alike Model (RALM) for Recommender Systems

The Real‑time Attention‑based Look‑alike Model (RALM) converts recommendation to a user‑user problem by representing items with aggregated seed‑user embeddings, employs shared projection, local and global attention towers, and enables instant, diverse, high‑CTR recommendations without retraining, as demonstrated by its deployment in WeChat “Look‑at”.

look-alikereal-time attentionrecommendation system
0 likes · 13 min read
Real-time Attention-based Look-alike Model (RALM) for Recommender Systems
360 Tech Engineering
360 Tech Engineering
Aug 8, 2019 · Artificial Intelligence

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

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

A/B testingSystem Designalgorithm engineering
0 likes · 9 min read
Recommendation System Optimization: Lessons, AB Testing Cycles, and Practical Principles
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
Big Data Technology & Architecture
Big Data Technology & Architecture
Jul 25, 2019 · Artificial Intelligence

Evolution of Weibo Recommendation Architecture: From Independent 1.0 to Platform 3.0

This article traces the evolution of Weibo's recommendation system architecture across three major stages—independent 1.0, layered 2.0, and platform‑style 3.0—detailing the environmental drivers, technical goals, component composition, advantages, shortcomings, and the concrete outcomes of each phase.

AIPlatform EvolutionWeibo
0 likes · 20 min read
Evolution of Weibo Recommendation Architecture: From Independent 1.0 to Platform 3.0
DataFunTalk
DataFunTalk
Jul 11, 2019 · Artificial Intelligence

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

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

AlibabaB2B2Ce‑commerce
0 likes · 17 min read
Alibaba Retail's Intelligent Recommendation System: Business Background, Architecture, Matching and Ranking Models
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 25, 2019 · Artificial Intelligence

How Alibaba Inserts Marketing Cards to Boost Recommendation Revenue

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

AlibabaCTR predictioncard insertion
0 likes · 8 min read
How Alibaba Inserts Marketing Cards to Boost Recommendation Revenue
DataFunTalk
DataFunTalk
Jun 13, 2019 · Artificial Intelligence

What Makes a Good Recommendation System?

This article explores the multifaceted criteria for evaluating a good recommendation system, covering macro and micro perspectives, product domain considerations, information retrieval, algorithmic accuracy, user experience, and business impact, and outlines a systematic iteration process for continuous improvement.

AIEvaluation MetricsUser experience
0 likes · 13 min read
What Makes a Good Recommendation System?
Ctrip Technology
Ctrip Technology
Jun 4, 2019 · Artificial Intelligence

Ctrip Search Recommendation System Architecture and Evolution

This article presents an overview of Ctrip's travel recommendation system, detailing its architecture, user‑behavior analysis, product catalog handling, various recall strategies, ranking methods—including machine‑learning models like XGBoost—and future directions toward deeper AI and NLP integration.

Ctripcollaborative filteringranking
0 likes · 9 min read
Ctrip Search Recommendation System Architecture and Evolution
58 Tech
58 Tech
May 31, 2019 · Artificial Intelligence

Summary of 58 Group Technical Salon: Recommendation System Architecture and Search Ranking Algorithm Practices

The article summarizes the 58 Group technical salon where experts presented the microservice‑based recommendation system architecture, data and strategy layers, and the internally built search ranking platform covering sampling, feature engineering, and model training, highlighting practical implementations and lessons learned.

AIMicroservicesdata pipeline
0 likes · 7 min read
Summary of 58 Group Technical Salon: Recommendation System Architecture and Search Ranking Algorithm Practices
Mafengwo Technology
Mafengwo Technology
May 17, 2019 · Backend Development

How We Built a Resilient Local Cache for a High‑Performance Recommendation System

When the recommendation service experiences database disconnections, third‑party timeouts, or network jitter, we designed an off‑heap local disaster‑recovery cache using OHC and SpringBoot that isolates cache logic, writes asynchronously, backs up to disk, and restores availability, keeping latency under 10 ms and improving user experience.

JavaOff-HeapSpringBoot
0 likes · 13 min read
How We Built a Resilient Local Cache for a High‑Performance Recommendation System
Architecture Digest
Architecture Digest
Apr 25, 2019 · Artificial Intelligence

Designing High‑Quality Recommendation Services: Principles and Strategies

This article explains how to build high‑performance, highly‑available, scalable, extensible, and secure recommendation services by outlining background concepts, defining quality criteria, discussing design challenges, and presenting concrete architectural principles and practical strategies.

AvailabilityScalabilitySecurity
0 likes · 29 min read
Designing High‑Quality Recommendation Services: Principles and Strategies
Efficient Ops
Efficient Ops
Mar 26, 2019 · Artificial Intelligence

How Live-Streaming Platforms Build Scalable Recommendation Systems

This article explains the design of a live‑streaming recommendation system, covering its overall architecture, ranking, content‑based and collaborative‑filtering methods, similarity calculations, multi‑algorithm fusion, sorting, user profiling, and evaluation metrics with practical examples and diagrams.

Evaluation Metricscollaborative filteringcontent-based
0 likes · 17 min read
How Live-Streaming Platforms Build Scalable Recommendation Systems
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
Xianyu Technology
Xianyu Technology
Feb 28, 2019 · Big Data

NVID Recommendation System Architecture and Technical Solutions

The NVID recommendation system for Taobao is built on a four‑layer architecture—activity material, configuration, business process, and application—and solves environment isolation, performance, audience management, and A/B testing challenges through optimized data schemas, ID mapping, multi‑level caching with database fallback, and real‑time user targeting, while future work aims at personalized audiences and automated ad optimization.

A/B testingBig DataSystem Architecture
0 likes · 11 min read
NVID Recommendation System Architecture and Technical Solutions
JD Tech
JD Tech
Feb 12, 2019 · Artificial Intelligence

Content‑Based Filtering: Concepts, Implementation, and Pros/Cons

The article explains content‑based filtering for recommendation systems, covering its basic concepts, feature requirements, implementation using vector representations and cosine similarity, advantages and disadvantages, and supplementary algorithms such as k‑Nearest Neighbor, Rocchio, decision trees, linear classifiers, and Naive Bayes.

Naive BayesRocchiocontent-based filtering
0 likes · 11 min read
Content‑Based Filtering: Concepts, Implementation, and Pros/Cons
21CTO
21CTO
Jan 16, 2019 · Artificial Intelligence

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

This article provides a comprehensive overview of Toutiao’s recommendation system, detailing its three‑dimensional modeling of content, user, and context, the feature extraction pipeline, real‑time training infrastructure, user‑tag generation, evaluation methodology, and content‑safety mechanisms.

Content SafetyReal-time Trainingevaluation
0 likes · 18 min read
Inside Toutiao’s Recommendation Engine: Architecture, Features, and Evaluation
DataFunTalk
DataFunTalk
Jan 8, 2019 · Artificial Intelligence

Yoo Video Bottom‑Page Recommendation System: From Zero to One Practice

This article details the end‑to‑end design, recall and ranking techniques, engineering implementation, and future research directions of Tencent's Yoo video bottom‑page recommendation system, illustrating how large‑scale video recommendation is built from business needs to deep learning models.

Embeddinglarge-scale systemsmachine learning
0 likes · 13 min read
Yoo Video Bottom‑Page Recommendation System: From Zero to One Practice
Alibaba Cloud Developer
Alibaba Cloud Developer
Dec 19, 2018 · Artificial Intelligence

How Cross‑Domain Embedding Boosts New User Recommendations in Alibaba’s Ecosystem

This article explains the design of a Cross‑Domain Embedding (CSDE) method that transfers Alipay user features to Taobao representations, details its learning and adaptive prediction stages, and shows experimental and online results demonstrating significant conversion‑rate improvements for new and inactive users.

Deep LearningGANconversion rate prediction
0 likes · 15 min read
How Cross‑Domain Embedding Boosts New User Recommendations in Alibaba’s Ecosystem
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.

AISystem Architecturecollaborative filtering
0 likes · 17 min read
Evolution and Architecture of Beike's Intelligent Real Estate Recommendation Platform
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
Manbang Technology Team
Manbang Technology Team
Oct 21, 2018 · Artificial Intelligence

AI and Deep Learning for Highway Freight Matching – Insights from QCon 2018

The article summarizes a QCon 2018 presentation by Luo Jingjia of Manbang Group, detailing how AI and deep‑learning techniques are applied to model, optimize, and recommend vehicle‑cargo matching in China’s massive highway logistics network, including data handling, neural‑network design, and practical challenges.

AIDeep Learningrecommendation system
0 likes · 18 min read
AI and Deep Learning for Highway Freight Matching – Insights from QCon 2018
21CTO
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
Qizhuo Club
Qizhuo Club
Sep 11, 2018 · Artificial Intelligence

How 360 Mobile Assistant Built a Scalable AI‑Powered App Recommendation System

This article details the design, architecture, and key components of 360 Mobile Assistant's recommendation system, covering business scenarios, data warehouse and computing layers, feature engineering, model selection, and online deployment strategies to improve app discovery and user engagement.

CTR predictionData Warehousefeature engineering
0 likes · 19 min read
How 360 Mobile Assistant Built a Scalable AI‑Powered App Recommendation System
JD Retail Technology
JD Retail Technology
Aug 24, 2018 · Artificial Intelligence

JD Showcases Ten Papers and DEERS Recommendation System at KDD 2018

At KDD 2018 in London, JD presented ten research papers—including a reinforcement‑learning based recommendation system called DEERS—highlighting its big‑data and AI capabilities across retail, supply‑chain, healthcare, and smart‑city applications while fostering industry‑academic collaboration.

AIKDD2018Retail analytics
0 likes · 8 min read
JD Showcases Ten Papers and DEERS Recommendation System at KDD 2018
Meitu Technology
Meitu Technology
Jul 17, 2018 · Artificial Intelligence

Video Clustering Techniques for Personalized Recommendation in Meipai

Meipai’s personalized recommendation system leverages massive user‑behavior data to build behavior‑driven video clusters—evolving from TopicModel through Item2vec and Keyword Propagation to a DSSM deep model—boosting ranking AUC, enhancing UI diversity, similar‑video search, niche discovery, and feature engineering.

DSSMItem2Veckeyword propagation
0 likes · 22 min read
Video Clustering Techniques for Personalized Recommendation in Meipai
Meitu Technology
Meitu Technology
Jun 13, 2018 · Artificial Intelligence

Meipai AI Tech Talk: Deep Ranking Models, Video Clustering, and Optimization

The talk covered Meipai’s personalized deep ranking model that balances depth and low latency, a behavior‑driven video clustering method that enriches recommendation beyond visual cues, and the use of advanced data structures to accelerate solving large‑scale optimization problems in business contexts.

Neural Networksmachine learningoptimization
0 likes · 5 min read
Meipai AI Tech Talk: Deep Ranking Models, Video Clustering, and Optimization
ITFLY8 Architecture Home
ITFLY8 Architecture Home
Jun 7, 2018 · Artificial Intelligence

How Modern Recommendation Systems Work: Architecture, Algorithms, and Best Practices

This article explains the goals, architectures, data pipelines, recall strategies, and ranking models of contemporary recommendation systems, covering both online and offline components, collaborative filtering, content-based methods, feature engineering, and practical interview insights for engineers.

Ranking Modelscollaborative filteringmachine learning
0 likes · 18 min read
How Modern Recommendation Systems Work: Architecture, Algorithms, and Best Practices
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
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.

Big Dataartificial intelligencee‑commerce
0 likes · 20 min read
How JD Built a Scalable AI-Powered Recommendation Engine for E‑Commerce
360 Quality & Efficiency
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 testingdata classificationmachine learning
0 likes · 4 min read
Fundamentals of Recommendation Engines: User Profiling, Data Classification, and Testing Methods
58 Tech
58 Tech
Feb 2, 2018 · Artificial Intelligence

Deep Learning Applications in 58.com Intelligent Recommendation System

This article details how 58.com leverages deep learning models such as FNN, Wide&Deep, CNN+DNN, and YouTube DNN recall, along with a custom AI platform, to enhance recommendation ranking and recall, achieving measurable improvements in click‑through rates and overall system performance.

CNNDNNFNN
0 likes · 13 min read
Deep Learning Applications in 58.com Intelligent Recommendation System
Alibaba Cloud Developer
Alibaba Cloud Developer
Feb 1, 2018 · Artificial Intelligence

How Alibaba’s Graph‑Based Bundle Mining Doubles Conversion in E‑Commerce

Alibaba’s latest bundle‑mining system leverages weighted graph embedding and real‑time sampling to recommend complementary products, replacing traditional item‑to‑item similarity, boosting click‑through rates by up to 13% offline and 4% online during the Double‑11 promotion while handling billions of edges.

Real-time Processingbundle mininge‑commerce
0 likes · 12 min read
How Alibaba’s Graph‑Based Bundle Mining Doubles Conversion in E‑Commerce
Architecture Digest
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 Safetycontent analysisevaluation
0 likes · 19 min read
Overview of Toutiao's Recommendation System: Architecture, Content Analysis, User Tagging, Evaluation, and Content Safety
21CTO
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 SafetyReal-time Trainingevaluation
0 likes · 18 min read
Inside Toutiao’s Recommendation Engine: Architecture, Features, and Evaluation
Alibaba Cloud Developer
Alibaba Cloud Developer
Dec 20, 2017 · Artificial Intelligence

How Alibaba Leverages Graph Embedding & Deep Learning for Double 11 Home‑Page Recommendations

This article explains how Alibaba's recommendation team built a large‑scale, AI‑driven personalization pipeline for the Double 11 shopping festival, using graph‑embedding recall, deep‑learning ranking models such as DeepResNet, DCN, and a custom XTensorflow platform to improve coverage, diversity, and click‑through rates.

AIDeep Learninge‑commerce
0 likes · 20 min read
How Alibaba Leverages Graph Embedding & Deep Learning for Double 11 Home‑Page Recommendations
Alibaba Cloud Developer
Alibaba Cloud Developer
Dec 19, 2017 · Operations

How Alibaba’s TPP Intelligent Scheduler Boosts Resource Utilization and Handles Double‑11 Traffic

The article details Alibaba's Taobao Personalization Platform (TPP) intelligent scheduling system, explaining its architecture, optimization algorithms, convergence logic, and performance results that dramatically improve CPU utilization and automate scaling during both regular operation and high‑traffic events like Double‑11.

AlibabaAuto Scalingcloud operations
0 likes · 21 min read
How Alibaba’s TPP Intelligent Scheduler Boosts Resource Utilization and Handles Double‑11 Traffic
21CTO
21CTO
Oct 6, 2017 · Artificial Intelligence

How Cosine Similarity Powers Movie Recommendations: A Python Guide

This tutorial explains various similarity metrics such as cosine similarity, Euclidean distance, Jaccard index, and Pearson correlation, demonstrates a Python function to compute user interest similarity, and shows how to generate movie recommendations with example code and output.

Cosine Similarityrecommendation systemsimilarity metrics
0 likes · 7 min read
How Cosine Similarity Powers Movie Recommendations: A Python Guide
21CTO
21CTO
Oct 4, 2017 · Artificial Intelligence

Build a Python Recommendation Engine from Scratch: Step‑by‑Step Guide

This tutorial walks you through the fundamentals of recommendation systems, explaining why they’re needed, the types of engines, collaborative filtering concepts, and provides a hands‑on Python implementation with sample data and code to build your own recommender from the ground up.

collaborative filteringcontent-based filteringrecommendation system
0 likes · 12 min read
Build a Python Recommendation Engine from Scratch: Step‑by‑Step Guide
Meitu Technology
Meitu Technology
Sep 28, 2017 · Industry Insights

Inside Meitu’s 6th Tech Salon: Deep Dive into Meipai’s Recommendation, Monitoring, and Live‑Streaming Architecture

The sixth Meitu Internet Technology Salon in Beijing showcased Meipai’s evolution, with senior engineers detailing the platform’s recommendation system, real‑time background segmentation, monitoring framework, live‑streaming and bullet‑screen architecture, offering practical insights and best‑practice lessons for building and optimizing large‑scale video services.

MeipaiVideo platformindustry insights
0 likes · 7 min read
Inside Meitu’s 6th Tech Salon: Deep Dive into Meipai’s Recommendation, Monitoring, and Live‑Streaming Architecture
21CTO
21CTO
Sep 11, 2017 · Backend Development

How We Scaled Headline Recommendation Data with MySQL, Redis, and Pipeline Optimizations

This article details the architecture and evolution of a headline recommendation system, covering data aggregation, storage strategies using MySQL and Redis, challenges with reload latency and memory usage, and the optimizations—including data separation, Redis migration, and query pipeline improvements—that enabled scalable, efficient backend operations.

Pipelinedata storagerecommendation system
0 likes · 14 min read
How We Scaled Headline Recommendation Data with MySQL, Redis, and Pipeline Optimizations
Meituan Technology Team
Meituan Technology Team
Jul 28, 2017 · Artificial Intelligence

Deep Learning Applications in Meituan‑Dianping Recommendation System

The paper describes Meituan‑Dianping’s two‑stage recommendation pipeline—recall and ranking—and how a Wide & Deep neural architecture, enriched with extensive user, item, and context features and trained with Adam and cross‑entropy loss, significantly boosts CTR and recommendation novelty, with future plans to add RNNs and reinforcement learning.

CTR predictionWide&Deepoptimization
0 likes · 21 min read
Deep Learning Applications in Meituan‑Dianping Recommendation System
21CTO
21CTO
Jul 26, 2017 · Artificial Intelligence

How Precise Recommendation Systems Work: Architecture and User Behavior Mining

This article explains the background, four‑layer architecture, offline data mining techniques, and various user‑behavior‑based methods—including interaction, similar‑user, and collaborative‑filtering approaches—used to build accurate Weibo post recommendation systems.

collaborative filteringlabel propagationrecommendation system
0 likes · 13 min read
How Precise Recommendation Systems Work: Architecture and User Behavior Mining
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
21CTO
21CTO
Apr 4, 2017 · Artificial Intelligence

How Vipshop Evolved Its Real-Time Personalized Recommendation Engine

This article recounts Wu Guanlin’s presentation on the evolution of Vipshop’s personalized recommendation system, detailing the technical challenges of real‑time predictions, the three generations of architecture, the four‑stage recommendation engine, and the VRE platform’s design for scalability and low latency.

Big DataSystem Architecturemachine learning
0 likes · 10 min read
How Vipshop Evolved Its Real-Time Personalized Recommendation Engine
21CTO
21CTO
Mar 22, 2017 · Artificial Intelligence

How Youku Tudou Revamped Its Video Recommendation Engine for Real‑Time Ranking

The Youku Tudou data team overhauled its video recommendation system by moving ranking from offline to online, detailing architectural changes, advantages, challenges, feature handling, offline evaluation, and model weight fusion to improve scalability and user experience.

AB testingAISystem Architecture
0 likes · 7 min read
How Youku Tudou Revamped Its Video Recommendation Engine for Real‑Time Ranking
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%
Ctrip Technology
Ctrip Technology
Jan 5, 2017 · Artificial Intelligence

Design and Implementation of a Billion‑Scale Generalized Recommendation System at Tencent Cloud

This article explains how Tencent built a billion‑scale, generalized recommendation system by designing a reusable algorithm library, deploying a low‑latency, highly available real‑time streaming platform (R2), and offering a cloud‑based recommendation engine that simplifies integration for internet businesses.

AIReal‑Time Computingcloud computing
0 likes · 11 min read
Design and Implementation of a Billion‑Scale Generalized Recommendation System at Tencent Cloud
21CTO
21CTO
Nov 6, 2016 · Artificial Intelligence

How to Build a Scalable AI-Powered Recommendation System with SOA

This article outlines a service‑oriented architecture for a high‑availability personalized recommendation platform, detailing the front‑end, back‑end, crawler, user‑profile modeling, data collection from logs and client events, and processing pipelines using technologies such as Node.js, Python, RabbitMQ/Kafka, MongoDB and TensorFlow.

SOATensorFlowdata pipeline
0 likes · 5 min read
How to Build a Scalable AI-Powered Recommendation System with SOA
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
Ctrip Technology
Ctrip Technology
Aug 19, 2016 · Big Data

Ctrip's Big Data Architecture and Personalized Recommendation System

This article describes how Ctrip transformed its traditional application architecture into a high‑concurrency, big‑data‑driven platform, detailing storage, compute, and business‑layer redesigns that enable massive data ingestion, real‑time user‑intent services, and a scalable personalized recommendation system.

Big DataCtripHadoop
0 likes · 14 min read
Ctrip's Big Data Architecture and Personalized Recommendation System
21CTO
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
Architecture Digest
Architecture Digest
Apr 14, 2016 · Big Data

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

This article presents Meituan Dianping's data‑driven precise marketing architecture, detailing a layered pyramid system, user profiling, budget monitoring, and two real‑world cases—potential user mining and a smart coupon engine—demonstrating how big‑data techniques improve marketing efficiency and ROI.

Data ArchitectureMeituanmachine learning
0 likes · 12 min read
Data‑Driven Precise Marketing: Architecture and Case Studies from Meituan Dianping
21CTO
21CTO
Apr 12, 2016 · Artificial Intelligence

Designing System and Personalized Recommendation Engines with Mahout and Spark

This article explains the architecture of both system-wide and personalized recommendation modules, compares three recommendation strategies, details the use of Apache Mahout for collaborative filtering with Java code examples, and discusses cold‑start solutions within a Spark‑Hadoop stack.

MahoutSparkcold start
0 likes · 15 min read
Designing System and Personalized Recommendation Engines with Mahout and Spark
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.

cold startdata miningevaluation
0 likes · 18 min read
10 Essential Tips for Building High‑Performance Intelligent Recommendation Systems