BestHub
Sign in
Tagged articles

recommendation system

328 articles · Page 4 of 4
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.

AICloud ComputingReal-Time 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.

Data PipelineFull-StackSOA
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.

CtripHadoopSpark
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.

Meituandata architecturemachine 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
21CTO
21CTO
Feb 27, 2016 · Artificial Intelligence

How User‑Based Collaborative Filtering Powers Modern Recommendation Systems

This article explains the fundamentals of recommendation algorithms, focusing on user‑based collaborative filtering, similarity metrics, neighbor selection, scoring methods, practical implementation with the MovieLens dataset, and common challenges such as popularity bias and dirty data.

collaborative filteringmachine learningmovie recommendation
0 likes · 12 min read
How User‑Based Collaborative Filtering Powers Modern Recommendation Systems
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
ITPUB
ITPUB
Jan 20, 2016 · Big Data

How Meizu Built an Agile Big Data Platform for Millions of Users

The Meizu Tech Open Day showcased the company's rapid evolution to a data‑driven mobile internet firm, detailing its DW1.0 and DW2.0 data‑warehouse architectures, recommendation pipelines, Spark adoption, and ELK‑based log analytics, while sharing practical lessons and future challenges.

ELKSparkbig data
0 likes · 11 min read
How Meizu Built an Agile Big Data Platform for Millions of Users
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 6, 2016 · Artificial Intelligence

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

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

RFM modelclassificationcollaborative filtering
0 likes · 8 min read
How to Build an End‑to‑End Marketplace Recommendation System: Product, Algorithms & Implementation
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
21CTO
21CTO
Nov 20, 2015 · Artificial Intelligence

How Meituan Builds and Optimizes Its Recommendation System

This article explains Meituan's end‑to‑end recommendation system architecture, data processing pipeline, candidate generation strategies, model training and online ranking techniques, illustrating how data, algorithms, and real‑time signals are combined to improve relevance and conversion.

AIMeituandata engineering
0 likes · 19 min read
How Meituan Builds and Optimizes Its Recommendation System
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.

Backendcloud architecturefrontend
0 likes · 15 min read
Inside Baidu Mobile’s Personalization: Recommendation Engine & Cloud Architecture
21CTO
21CTO
Oct 26, 2015 · Artificial Intelligence

How Weibo’s Recommendation Engine Evolved: From 1.0 to Platform‑Scale 3.0

This article traces the evolution of Weibo's recommendation architecture across three major phases—independent 1.0, layered 2.0, and platform‑centric 3.0—detailing the driving business and technical factors, architectural components, advantages, shortcomings, and key outcomes of each stage.

AI EngineeringWeiboarchitecture evolution
0 likes · 19 min read
How Weibo’s Recommendation Engine Evolved: From 1.0 to Platform‑Scale 3.0
21CTO
21CTO
Oct 24, 2015 · Artificial Intelligence

Building an Offline Recommendation System with Mahout: Practical Steps and Tips

This article walks through the end‑to‑end process of building an offline recommendation system using Mahout, covering data collection, filtering, storage, various collaborative‑filtering algorithms, similarity measures, evaluation metrics, parameter tuning, AB testing, and spam‑fighting strategies.

Mahoutcollaborative filteringmachine learning
0 likes · 16 min read
Building an Offline Recommendation System with Mahout: Practical Steps and Tips
21CTO
21CTO
Oct 15, 2015 · Backend Development

How Weibo’s Recommendation Engine Evolved: From Isolated 1.0 to Platform‑Scale 3.0

This article traces the evolution of Weibo’s recommendation architecture across three major phases—independent 1.0, layered 2.0, and platform‑centric 3.0—detailing the environmental drivers, technical components, advantages, shortcomings, and key outcomes of each stage.

Scalable DesignWeibobackend development
0 likes · 19 min read
How Weibo’s Recommendation Engine Evolved: From Isolated 1.0 to Platform‑Scale 3.0
Architect
Architect
Oct 15, 2015 · Databases

Lushan: An Offline Static Data Storage Server for Recommendation Systems

This article details the design, implementation, and performance of Lushan, a high‑throughput offline static data storage server built with libevent that supports dynamic library mounting, key‑value indexing, and efficient query handling for large‑scale recommendation workloads.

C++High PerformanceKey-Value
0 likes · 18 min read
Lushan: An Offline Static Data Storage Server for Recommendation Systems
21CTO
21CTO
Sep 28, 2015 · Artificial Intelligence

How Meituan Built a Scalable AI‑Powered Recommendation Engine

This article details Meituan's end‑to‑end recommendation system, covering its four‑layer architecture, data sources, candidate‑generation strategies, fusion methods, and both linear and non‑linear re‑ranking models, while highlighting practical optimizations like AB testing and online learning.

Meituandata pipelinesmachine learning
0 likes · 15 min read
How Meituan Built a Scalable AI‑Powered Recommendation Engine
21CTO
21CTO
Sep 8, 2015 · Artificial Intelligence

Inside Meituan’s Recommendation Engine: From Data to Real‑Time Ranking

This article outlines Meituan’s end‑to‑end recommendation system, describing its data layer, candidate‑generation triggers, fusion strategies, and machine‑learning‑based ranking models—including collaborative filtering, location‑based, query‑based, graph‑based methods, and both linear and non‑linear models—while highlighting practical optimizations such as AB testing, real‑time behavior handling, and fallback strategies.

Meituancandidate generationcollaborative filtering
0 likes · 19 min read
Inside Meituan’s Recommendation Engine: From Data to Real‑Time Ranking
Art of Distributed System Architecture Design
Art of Distributed System Architecture Design
Aug 21, 2015 · Artificial Intelligence

Facebook’s Distributed Recommendation System: Architecture, Algorithms, and Performance

The article explains how Facebook built a large‑scale distributed recommendation system using Apache Giraph, collaborative filtering with matrix factorization, SGD and ALS algorithms, a novel work‑to‑work communication scheme, and performance optimizations that achieve ten‑fold speedups on billions of ratings.

ALSApache GiraphFacebook
0 likes · 9 min read
Facebook’s Distributed Recommendation System: Architecture, Algorithms, and Performance
21CTO
21CTO
Aug 14, 2015 · Artificial Intelligence

How Meituan Supercharges Local Services with Advanced Recommendation and Ranking

This article details Meituan's recommendation ecosystem, covering its key products, system goals, architecture, data pipelines, algorithms, cold‑start strategies, and the extensive ranking work—including modeling, sampling, bias removal, feature engineering, interleaving, and online learning—to dramatically boost user conversion.

cold startfeature engineeringranking
0 likes · 15 min read
How Meituan Supercharges Local Services with Advanced Recommendation and Ranking