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DataFunTalk
DataFunTalk
Aug 8, 2019 · Artificial Intelligence

Practical Experience of JD E‑commerce Recommendation System: Architecture, Ranking, Real‑time Updates, and Experiment Platform

This article shares JD's e‑commerce recommendation system practice, covering the overall online/offline architecture, recall and ranking modules, real‑time feature and model updates, multi‑objective and diversity strategies, first‑stage index‑based ranking, KNN recall, and a layered experiment platform for rapid iteration.

Learning-to-RankReal-Timee‑commerce
0 likes · 14 min read
Practical Experience of JD E‑commerce Recommendation System: Architecture, Ranking, Real‑time Updates, and Experiment Platform
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 2, 2019 · Artificial Intelligence

Alibaba’s AI Breakthroughs at KDD 2019: From CTR Prediction to Graph Learning

This article summarizes Alibaba’s twelve KDD 2019 papers, covering advances in long‑sequence CTR modeling, fashion recommendation, sponsored search, exact‑K recommendation, meta‑learning, transfer learning, scalable graph convolution, heterogeneous graph neural networks, knowledge‑driven product description, and related workshops, highlighting both algorithmic innovations and industrial deployments.

AISparse Datagraph neural networks
0 likes · 20 min read
Alibaba’s AI Breakthroughs at KDD 2019: From CTR Prediction to Graph Learning
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.comSystem Architecturee‑commerce
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.

@DataAlgorithmse‑commerce
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 8, 2019 · Artificial Intelligence

Deep Learning Ranking System and Model for NetEase News Feed Personalization

This article presents the design, optimization, and deployment of a deep‑learning based ranking pipeline and model for NetEase News, covering offline and online pipelines, feature‑processing enhancements, custom TensorFlow operators, modular model architectures, and performance improvements demonstrated in production.

AIPipelinefeature processing
0 likes · 11 min read
Deep Learning Ranking System and Model for NetEase News Feed Personalization
Beike Product & Technology
Beike Product & Technology
Jun 28, 2019 · Artificial Intelligence

Building a Comprehensive Tagging System for Real‑Estate Recommendation at Beike

This article explains how Beike, China’s largest residential service platform, leverages its massive house, client, and text data to design a multi‑layered tag architecture, detailing data sources, tag construction methods—including classification, keyword, geographic, anonymous topic, and temporal tags—and their application to improve personalized house search and recommendation.

NLPReal EstateTagging
0 likes · 14 min read
Building a Comprehensive Tagging System for Real‑Estate Recommendation at Beike
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 testEmbeddinghomestay
0 likes · 13 min read
Embedding‑Based Item‑to‑Item Recommendation for Homestay Platforms
Beike Product & Technology
Beike Product & Technology
May 23, 2019 · Artificial Intelligence

Applying Knowledge Graph Technology to Real Estate Search: Product Overview and Technical Architecture

This article introduces the "Kelu Fang" product, which leverages knowledge graph, NLU, and ranking technologies to enhance real‑estate search by adding commute‑based filtering and a local view of surrounding facilities, and discusses its architecture, implementation details, and future improvement directions.

AINLUReal Estate
0 likes · 11 min read
Applying Knowledge Graph Technology to Real Estate Search: Product Overview and Technical Architecture
DataFunTalk
DataFunTalk
Apr 19, 2019 · Artificial Intelligence

E-commerce Search and User Guidance: Concepts, Techniques, and Product Design

This article examines the role of search as a user guidance channel in e-commerce, outlining product requirements, user flow stages, and various algorithmic solutions—including query understanding, suggestion, rewriting, retrieval, and ranking—while also comparing implementations across major Chinese platforms.

Query Understandinge‑commercemachine learning
0 likes · 29 min read
E-commerce Search and User Guidance: Concepts, Techniques, and Product Design
Alibaba Cloud Developer
Alibaba Cloud Developer
Apr 8, 2019 · Artificial Intelligence

How Alibaba Builds a Massive E‑Commerce Concept Graph to Power Search & Recommendation

This article explains how Alibaba’s Search & Recommendation team constructs a large‑scale e‑commerce concept graph—defining e‑commerce concepts, mining them from queries and titles, building an ontology, linking concepts to entities, and applying the graph to improve personalized search and recommendation.

Ontologyconcept mininge‑commerce
0 likes · 19 min read
How Alibaba Builds a Massive E‑Commerce Concept Graph to Power Search & Recommendation
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
DataFunTalk
DataFunTalk
Jan 4, 2019 · Artificial Intelligence

AI‑Powered Automated Advertising Platform: 360 Easy Placement Overview

This article presents the design and technical details of 360 Easy Placement, an AI‑driven end‑to‑end advertising platform that automates creative generation, fast review, and optimization, addressing the challenges faced by small‑and‑medium advertisers through data‑rich models, multi‑task learning, and intelligent scene recommendation.

AIadvertising automationcreative generation
0 likes · 20 min read
AI‑Powered Automated Advertising Platform: 360 Easy Placement Overview
DataFunTalk
DataFunTalk
Dec 28, 2018 · Artificial Intelligence

Zhihu Recommendation Page Ranking: Architecture, Feature Engineering, Model Evolution, and Future Directions

This article presents a comprehensive overview of Zhihu's recommendation page ranking system, covering its request flow, historical ranking evolution, feature design, deep learning models, multi‑task CTR optimization, practical engineering insights, current challenges, and future research directions such as reinforcement learning.

CTRmulti-task learningranking
0 likes · 15 min read
Zhihu Recommendation Page Ranking: Architecture, Feature Engineering, Model Evolution, and Future Directions
DataFunTalk
DataFunTalk
Dec 27, 2018 · Artificial Intelligence

Construction and Application of a Tourism Knowledge Graph

This article explains what a tourism knowledge graph is, discusses its architecture, construction methods, practical applications such as QA and recommendation, and explores future directions integrating knowledge graphs with deep learning and multi‑domain fusion.

AINLPTourism
0 likes · 10 min read
Construction and Application of a Tourism Knowledge Graph
Suning Technology
Suning Technology
Dec 17, 2018 · Artificial Intelligence

How Search & Recommendation Technologies Evolve: Insights from Suning’s 2018 Conference

The 2018 Suning Search & Recommendation Technology Conference in Nanjing gathered over 400 industry experts to discuss search engine evolution, recommendation algorithm models, multi‑source data fusion, multimedia video retrieval, and AI‑driven advertising, highlighting practical implementations and future research directions.

data fusionmachine learningrecommendation
0 likes · 5 min read
How Search & Recommendation Technologies Evolve: Insights from Suning’s 2018 Conference
DataFunTalk
DataFunTalk
Nov 7, 2018 · Artificial Intelligence

Evolution of Ele.me Recommendation Algorithms and Online Learning Practice

This article outlines the background of Ele.me's recommendation business, details the evolution of its recommendation algorithms from rule‑based models to deep learning and online learning, and explains the practical implementation of real‑time data pipelines, feature engineering, model training, and deployment.

Ele.meOnline Learningmachine learning
0 likes · 13 min read
Evolution of Ele.me Recommendation Algorithms and Online Learning Practice
HomeTech
HomeTech
Oct 29, 2018 · Artificial Intelligence

Applying ListNet Listwise Ranking Model for Car Purchase Intent Prediction

This article introduces the ListNet listwise ranking algorithm, explains its theoretical foundations and loss function, presents a Python implementation with gradient computation, and demonstrates its superior performance over pointwise and pairwise models on public benchmarks and an internal automotive dataset for predicting users' intended car series.

Learning-to-Ranklistnetmachine-learning
0 likes · 14 min read
Applying ListNet Listwise Ranking Model for Car Purchase Intent Prediction
Alibaba Cloud Developer
Alibaba Cloud Developer
Sep 30, 2018 · Artificial Intelligence

How Alibaba’s Search & Recommendation Evolved: From Rules to Cognitive AI

This article reviews the evolution of Alibaba’s e‑commerce search and recommendation technologies, detailing Taobao’s unique challenges, the shift from rule‑based retrieval to large‑scale machine learning, real‑time online learning, deep learning and intelligent decision‑making, and outlines future directions toward cognitive intelligence.

Deep Learningcognitive AIe‑commerce
0 likes · 16 min read
How Alibaba’s Search & Recommendation Evolved: From Rules to Cognitive AI
Ctrip Technology
Ctrip Technology
Sep 27, 2018 · Artificial Intelligence

Application of Knowledge Graphs in the Internet Tourism Industry

This article examines the distinctive features of tourism-domain knowledge graphs, outlines methods for constructing them from internal and external data sources, and explores their practical applications such as question‑answering bots, personalized recommendation, and advanced search within the online travel sector.

AIGraph DatabaseTourism
0 likes · 11 min read
Application of Knowledge Graphs in the Internet Tourism Industry
Manbang Technology Team
Manbang Technology Team
Sep 15, 2018 · Artificial Intelligence

YMM-TECH Algorithm Competition Final: Problem Background, Evaluation Methodology, and Scoring Details

The YMM-TECH algorithm competition final, held at Nanjing University of Posts and Telecommunications, presented a logistics recommendation problem that leverages driver behavior data, evaluates solutions using ranking‑accuracy metrics with position‑weighted scores, and provides detailed formulas, examples, and data‑driven recommendations for 20 cargo items per driver.

AIEvaluation Metricsalgorithm competition
0 likes · 5 min read
YMM-TECH Algorithm Competition Final: Problem Background, Evaluation Methodology, and Scoring Details
Alibaba Cloud Developer
Alibaba Cloud Developer
Sep 14, 2018 · Artificial Intelligence

How Alibaba’s UC Team Boosted Short‑Video Recommendations with FM+GBM

This article details the evolution of Alibaba's short‑video feed ranking system, from a Wide&Deep CTR model to a hybrid Factorization‑Machine and Gradient‑Boosted‑Tree approach, describing feature engineering, model architecture, experimental results, lessons learned, and future directions toward duration‑based relevance.

factorization machinesgradient boostingmachine learning
0 likes · 11 min read
How Alibaba’s UC Team Boosted Short‑Video Recommendations with FM+GBM
Alibaba Cloud Developer
Alibaba Cloud Developer
Sep 6, 2018 · Artificial Intelligence

How Wide‑ResNet with Batch Norm Boosts 1688’s ‘You May Like’

This article introduces the Wide&Deep, PNN, DeepFM, and a novel Wide‑ResNet model applied to Alibaba’s 1688 “You May Like” recommendation, describes the system architecture, training data, experimental results showing AUC improvements with batch normalization, and shares practical tuning insights.

Batch NormalizationResNetdeepfm
0 likes · 12 min read
How Wide‑ResNet with Batch Norm Boosts 1688’s ‘You May Like’
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 %.

EngineeringVector SearchXianyu
0 likes · 15 min read
Personalized Recommendation for Xianyu Small Item Pools: Challenges and Solutions
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 28, 2018 · Artificial Intelligence

Boosting 1688’s “Guess You Like” with Wide‑ResNet and Batch Normalization

This article introduces Wide&Deep, PNN, DeepFM, and a novel Wide‑ResNet model for Alibaba’s 1688 “Guess You Like” recommendation, explains the underlying feature services and real‑time scoring pipeline, presents offline experiments showing AUC gains with batch normalization, and shares practical tuning insights.

AlibabaCTR predictionDeep Learning
0 likes · 13 min read
Boosting 1688’s “Guess You Like” with Wide‑ResNet and Batch Normalization
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 22, 2018 · Artificial Intelligence

Boosting Short Video Recommendations with Multi‑Objective Weighted Logistic Regression

This article explains how short‑video platforms enhance recommendation quality by combining click‑through‑rate models with multi‑objective optimization of watch time and completion rate, using sample reweighting and weighted logistic regression to balance perceived and real relevance while improving offline AUC and online user engagement.

CTRmulti-objective optimizationrecommendation
0 likes · 10 min read
Boosting Short Video Recommendations with Multi‑Objective Weighted Logistic Regression
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
DataFunTalk
DataFunTalk
Jul 2, 2018 · Artificial Intelligence

Overview of Sogou Information Feed Recommendation Algorithms

This article summarizes Sogou's information‑feed recommendation system, covering the architecture from data collection and NLP processing to recall, ranking, and feedback, and detailing the classification, tagging, keyword extraction, and various recall and ranking models such as FastText, TextCNN, collaborative filtering, and wide‑and‑deep learning.

NLPSogouinformation feed
0 likes · 14 min read
Overview of Sogou Information Feed Recommendation Algorithms
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
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 14, 2018 · Artificial Intelligence

Self-Attention Boosts Heterogeneous User Behavior Modeling for Recommendations

This paper proposes a novel attention‑based framework that groups and encodes heterogeneous user behavior sequences into separate semantic subspaces, applies self‑attention to capture inter‑behavior influences, and demonstrates faster training and comparable or improved recommendation performance across multiple tasks and datasets.

Self-Attentionheterogeneous behaviormulti-task learning
0 likes · 12 min read
Self-Attention Boosts Heterogeneous User Behavior Modeling for Recommendations
DataFunTalk
DataFunTalk
Jun 13, 2018 · Artificial Intelligence

Evolution of E‑commerce Platform Recommendation System Architecture

This article reviews the evolution of recommendation system architecture for C2C e‑commerce platforms, tracing stages from simple offline‑online pipelines through granular feed‑flow improvements, real‑time processing, and machine‑learning‑driven models, while highlighting user‑profile construction, challenges, and best‑practice guidelines.

Real-Timearchitecturee‑commerce
0 likes · 10 min read
Evolution of E‑commerce Platform Recommendation System Architecture
360 Quality & Efficiency
360 Quality & Efficiency
May 11, 2018 · Artificial Intelligence

Common Engineering Algorithms and Their Testing Methods

This article introduces the most commonly used algorithms in engineering—recommendation, optimization, estimation, and classification—explains their typical application scenarios, and discusses various testing methods and evaluation metrics such as offline experiments, user surveys, A/B testing, and performance indicators like accuracy, coverage, diversity, and robustness.

Evaluationalgorithmmachine learning
0 likes · 12 min read
Common Engineering Algorithms and Their Testing Methods
Alibaba Cloud Developer
Alibaba Cloud Developer
Apr 28, 2018 · Artificial Intelligence

How Alibaba Uses AI to Power the New Retail Ecosystem

Alibaba’s recent Search and Computing Open Day revealed how AI, big‑data algorithms and multi‑objective optimization are reshaping its e‑commerce ecosystem, boosting commercial efficiency, supporting diverse merchants, and driving innovative retail experiences for billions of users.

AIAlgorithmsAlibaba
0 likes · 18 min read
How Alibaba Uses AI to Power the New Retail Ecosystem
AntTech
AntTech
Jan 23, 2018 · Artificial Intelligence

Privacy-Preserving Point-of-Interest Recommendation via Decentralized Matrix Factorization

This article summarizes a AAAI 2018 paper that introduces a privacy‑preserving, decentralized matrix‑factorization approach for point‑of‑interest recommendation, detailing its problem definition, model design, random‑walk based user interaction, experimental evaluation on Foursquare and Alipay datasets, and future research directions.

AIdecentralized learningmatrix factorization
0 likes · 10 min read
Privacy-Preserving Point-of-Interest Recommendation via Decentralized Matrix Factorization
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.

EvaluationReal-Timecold start
0 likes · 15 min read
Overview of Recommendation Systems: Goals, Methods, Architecture, and Practical Considerations
Qunar Tech Salon
Qunar Tech Salon
Aug 21, 2017 · Artificial Intelligence

Tourism Comment Text Mining and Recommendation System Using NLP and Big Data

This article presents a comprehensive NLP‑driven text‑mining workflow for tourism comment data, covering data cleaning, word2vec training, keyword extraction, sentiment analysis, ranking, and a lightweight architecture that enables fast, accurate recommendation of scenic spots for users.

NLPSentiment Analysismachine learning
0 likes · 5 min read
Tourism Comment Text Mining and Recommendation System Using NLP and Big Data
Qunar Tech Salon
Qunar Tech Salon
Aug 16, 2017 · Artificial Intelligence

Applying Wide & Deep Learning to Meituan‑Dianping Recommendation System

This article describes how Meituan‑Dianping leverages deep learning, especially the Wide & Deep model, to improve its recommendation system by addressing business diversity, user context, feature engineering challenges, optimizer and loss function choices, and presents offline and online experimental results showing significant CTR gains.

CTRDeep LearningWide&Deep
0 likes · 22 min read
Applying Wide & Deep Learning to Meituan‑Dianping Recommendation System
21CTO
21CTO
Jul 17, 2017 · Artificial Intelligence

Inside 58.com’s Smart Recommendation Engine: Architecture, Algorithms, Data

58.com’s intelligent recommendation system, evolving from a C++ monolith in 2014 to a Java-based micro‑service platform, integrates multi‑layer data processing, diverse recall and ranking algorithms, and a robust microservice architecture to deliver personalized listings across housing, jobs, cars, and more.

Microservicesdata engineeringranking
0 likes · 27 min read
Inside 58.com’s Smart Recommendation Engine: Architecture, Algorithms, Data
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 9, 2017 · Artificial Intelligence

Migrating Youku Tudou Video Recommendation System from Offline to Online Sorting

The article details how Youku Tudou redesigned its video recommendation architecture, moving ranking from offline to online processing, outlining the comparative architecture, benefits, challenges, feature handling, offline evaluation methods, and weight‑fusion techniques that enabled a successful launch after two months of development.

AB testingAUC evaluationfeature engineering
0 likes · 7 min read
Migrating Youku Tudou Video Recommendation System from Offline to Online Sorting
Ctrip Technology
Ctrip Technology
Jan 5, 2017 · Artificial Intelligence

Design and Implementation of Ele.me's Fast‑Iterating Online Recommendation System

This article details how Ele.me built a rapidly iterating recommendation system, covering model ranking architectures (single, linear, multi), online feature computation pipelines, feature management, and shuffling logic to balance algorithmic relevance with user perception, providing practical insights for large‑scale personalized services.

feature-engineeringmachine-learningonline system
0 likes · 13 min read
Design and Implementation of Ele.me's Fast‑Iterating Online Recommendation System
Alibaba Cloud Developer
Alibaba Cloud Developer
Dec 7, 2016 · Artificial Intelligence

How Online AI Transforms Search and Recommendation Systems

At Alibaba's 2016 Double 11 Tech Forum, researcher Xu Yinghui presented how online AI technologies enhance search and recommendation on the e‑commerce platform, turning massive user behavior data into actionable insights that improve traffic allocation and maximize welfare for consumers, sellers, and the platform.

AIAlibabae‑commerce
0 likes · 2 min read
How Online AI Transforms Search and Recommendation Systems
Ctrip Technology
Ctrip Technology
Sep 19, 2016 · Artificial Intelligence

Personalized Demand Prediction and Ranking for Qunar's "Guess You Like" Feature

This article describes Qunar's personalized demand prediction system for the "Guess You Like" card, detailing how user‑demand associations are mined via rule engines, collaborative filtering, LBS and manual rules, and how ranking models evolve from subjective Bayes to RankBoost and LambdaMart, with experimental evaluation and future LSTM plans.

AITravelmachine learning
0 likes · 10 min read
Personalized Demand Prediction and Ranking for Qunar's "Guess You Like" Feature
Architecture Digest
Architecture Digest
Sep 12, 2016 · Artificial Intelligence

Design and Implementation of a Real‑Time, Highly Available General Recommendation Platform at YHD

The article describes how YHD's precision recommendation team built a real‑time, highly available, traceable general recommendation platform, detailing its background, overall architecture, visual configuration and traceability subsystems, and reporting significant improvements in development speed, reuse and user satisfaction.

AIHBaseKafka
0 likes · 8 min read
Design and Implementation of a Real‑Time, Highly Available General Recommendation Platform at YHD
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.

Qunarcollaborative filteringmachine learning
0 likes · 14 min read
Personalized Demand Prediction and Ranking for Qunar App’s “You May Like” Card
ITPUB
ITPUB
Jun 11, 2016 · Big Data

How 58 Daojia Leverages User Portraits to Boost Operations and Fight Fraud

This article details 58 Daojia's data‑driven approach to building user‑portrait tags, covering tag construction, evaluation, and practical applications such as personalized recommendations, anti‑fraud measures, coupon distribution, and dynamic pricing, while outlining the underlying big‑data architecture and technical challenges.

Big Dataanti-frauddata mining
0 likes · 18 min read
How 58 Daojia Leverages User Portraits to Boost Operations and Fight Fraud
21CTO
21CTO
Jun 11, 2016 · Artificial Intelligence

Designing System & Personalized Recommendations Using Mahout

This article outlines the design of both system-wide and personalized recommendation modules for e‑commerce platforms, explains three recommendation approaches (demographic, content‑based, collaborative filtering), details the implementation of Mahout’s collaborative‑filtering algorithm with Java code, discusses data sources, technology stack, algorithm choices, and solutions to the cold‑start problem.

Mahoutcollaborative filteringe‑commerce
0 likes · 14 min read
Designing System & Personalized Recommendations Using Mahout
Architecture Digest
Architecture Digest
May 11, 2016 · Artificial Intelligence

Interest Feeds: From Facebook NewsFeed and EdgeRank to Pinterest Smart Feed and General Techniques

This article explains why interest‑driven feeds are essential, reviews Facebook's NewsFeed evolution and EdgeRank algorithm, details Pinterest's Smart Feed architecture and Pinnability model, and provides a comprehensive guide to building, ranking, and monitoring generic interest‑feed systems for social platforms.

FacebookPinterestSocial network
0 likes · 34 min read
Interest Feeds: From Facebook NewsFeed and EdgeRank to Pinterest Smart Feed and General Techniques
Architect
Architect
Feb 26, 2016 · Artificial Intelligence

User-Based Collaborative Filtering Recommendation Algorithm Explained

This article introduces the concept and history of recommendation algorithms, outlines the basic conditions for recommendations, and provides a detailed explanation of user-based collaborative filtering, including similarity calculations, neighbor selection, recommendation scoring, practical code snippets, and discussion of potential issues.

algorithmcollaborative filteringrecommendation
0 likes · 12 min read
User-Based Collaborative Filtering Recommendation Algorithm Explained
Architect
Architect
Feb 1, 2016 · Big Data

An Introduction to Data Mining Algorithms and Their Real-World Applications

This article introduces the main types of data‑mining algorithms—classification, prediction, clustering, and association—explains supervised and unsupervised learning, and illustrates each with practical examples such as spam detection, tumor identification, wine quality assessment, fraud detection, recommendation systems, and authorship analysis.

anomaly detectionclassificationdata mining
0 likes · 14 min read
An Introduction to Data Mining Algorithms and Their Real-World Applications
Qunar Tech Salon
Qunar Tech Salon
Jan 13, 2016 · Artificial Intelligence

Ranking Learning in Mobile Taobao: Challenges, Solutions, and Improvements

This article presents a comprehensive overview of ranking learning techniques used in Mobile Taobao's recommendation system, covering problem definition, pointwise/pairwise/listwise approaches, feature engineering, online learning, industry applications, and future optimization strategies.

CTR predictionLambdaMARTlistwise
0 likes · 8 min read
Ranking Learning in Mobile Taobao: Challenges, Solutions, and Improvements
Architects Research Society
Architects Research Society
Dec 17, 2015 · Artificial Intelligence

How Search Engine Experience Informs Personalized Recommendation at Toutiao

The article explains how search engine techniques such as large‑scale candidate recall, fine‑grained ranking, user profiling, and multi‑objective optimization are applied to news personalization at Toutiao, highlighting data sampling, machine‑learning pipelines, challenges of news freshness, and architectural evolution.

multi-objective optimizationnews recommendationrecommendation
0 likes · 5 min read
How Search Engine Experience Informs Personalized Recommendation at Toutiao
21CTO
21CTO
Nov 5, 2015 · Artificial Intelligence

Inside Weibo’s Recommendation Engine: Architecture, Layers, and Key Technologies

This article outlines the four‑layer architecture of Weibo’s recommendation system—front‑end (RFront), application (RApp), compute (RCompute), and data (RStore)—detailing each layer’s purpose, technologies such as CRF, nginx+Lua, C++ frameworks, and supporting tools for logging, monitoring, and evaluation.

AIWeibobackend-development
0 likes · 10 min read
Inside Weibo’s Recommendation Engine: Architecture, Layers, and Key Technologies
Art of Distributed System Architecture Design
Art of Distributed System Architecture Design
Oct 16, 2015 · Artificial Intelligence

Building Machine Learning Systems in Small Teams: Practices, Pitfalls, and Lessons from Dangdang

This talk shares the experience of a small machine‑learning team at Dangdang, describing how they built a recommendation system from scratch, the tools and processes they used, the challenges of limited personnel, and the many pitfalls they encountered while iterating toward a production‑ready solution.

ML pipelineTechnical Debtbest practices
0 likes · 21 min read
Building Machine Learning Systems in Small Teams: Practices, Pitfalls, and Lessons from Dangdang
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.

moderationpersonalizationquora
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.

Meituanmachine learningpersonalization
0 likes · 14 min read
Meituan Recommendation System Architecture and Optimization Practices