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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.

Machine Learningcold startdata mining
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.

Machine Learningcollaborative filteringmovie 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.

Machine Learninge-commercepersonalization
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.

Big DataData ArchitectureData Warehouse
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.

Machine LearningWeChat advertisingcontent-based filtering
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.

AIe-commercepersonalization
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.

AIMachine LearningMeituan
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.

Frontendbackendcloud architecture
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 EngineeringMachine LearningWeibo
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.

Machine LearningMahoutcollaborative filtering
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.

Backend DevelopmentScalable DesignWeibo
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.

CKey-Valuehigh-performance
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.

Machine LearningMeituanOnline 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.

MeituanOnline Learningcandidate generation
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