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Recommendation Systems

489 articles · Page 5 of 5
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 16, 2019 · Artificial Intelligence

How IntentGC Scales Graph Convolution for Billion‑Node Recommendation Systems

IntentGC, a KDD 2019 paper, introduces a scalable graph convolution framework that fuses explicit user‑item interactions with rich heterogeneous signals to tackle link‑prediction on billion‑node e‑commerce graphs, offering efficient training, dual‑convolution design, and superior performance over existing baselines.

IntentGCRecommendation Systemsgraph convolution
0 likes · 10 min read
How IntentGC Scales Graph Convolution for Billion‑Node Recommendation Systems
DataFunTalk
DataFunTalk
Aug 16, 2019 · Artificial Intelligence

Tree‑based Deep Match (TDM): Design, Implementation, and Applications in Large‑Scale Retrieval

This article presents a comprehensive overview of the Tree‑based Deep Match (TDM) algorithm, describing the evolution of retrieval technology, the limitations of traditional Match‑Rank pipelines, the design of a one‑stage tree‑indexed deep matching model, its training methodology, performance gains on public datasets, and its deployment in Alibaba’s advertising and e‑commerce platforms.

Recommendation SystemsRetrievalTDM
0 likes · 23 min read
Tree‑based Deep Match (TDM): Design, Implementation, and Applications in Large‑Scale Retrieval
DataFunTalk
DataFunTalk
Aug 14, 2019 · Artificial Intelligence

Understanding Recommendation Systems: From Information Overload to Personalized AI Solutions

The article explores how the rapid growth of the internet has created information overload, discusses the challenges of recommendation systems such as sparsity and timeliness, outlines a four‑step personalized content pipeline, and highlights the interdisciplinary nature of building effective AI‑driven recommendation solutions.

AIBig DataData Engineering
0 likes · 16 min read
Understanding Recommendation Systems: From Information Overload to Personalized AI Solutions
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 14, 2019 · Artificial Intelligence

How MIMN+UIC Breaks the Long-Sequence Barrier in Real-Time CTR Prediction

This article presents a co-designed algorithm‑system solution—MIMN and an independent UIC module—that enables ultra‑long user behavior modeling for click‑through rate prediction, delivering significant offline AUC gains and online CTR/RPM improvements in Alibaba's display advertising platform.

CTR predictionRecommendation Systemsdeep learning
0 likes · 12 min read
How MIMN+UIC Breaks the Long-Sequence Barrier in Real-Time CTR Prediction
dbaplus Community
dbaplus Community
Jul 27, 2019 · Fundamentals

How a Chinese Drama Illustrates Data Structures, Algorithms, and Time Complexity

The article uses the plot of the historical series “The Longest Day in Chang’an” to explain how proper use of data structures, recommendation algorithms, and time‑complexity optimizations—such as O(n²) brute‑force search, O(n) mapping, and O(log n) spatial tricks—can turn a desperate race against time into a successful mission, while also touching on big‑data analysis and simple encryption via the tower‑signal system.

AlgorithmsRecommendation Systemsencryption
0 likes · 4 min read
How a Chinese Drama Illustrates Data Structures, Algorithms, and Time Complexity
DataFunTalk
DataFunTalk
Jul 3, 2019 · Artificial Intelligence

Improving Recommendation Diversity with Determinantal Point Processes and Greedy Optimization

The article explains how recommendation systems balance exploitation and exploration, introduces diversity metrics such as temporal, spatial, and coverage, and presents a determinantal point process (DPP) based algorithm accelerated by Cholesky decomposition and greedy inference, demonstrating significant speedups and improved relevance‑diversity trade‑offs in experiments.

DiversityOptimizationRecommendation Systems
0 likes · 10 min read
Improving Recommendation Diversity with Determinantal Point Processes and Greedy Optimization
DataFunTalk
DataFunTalk
Jul 1, 2019 · Artificial Intelligence

Data-Driven Foundations for Building Recommendation Systems

The article explains how data serves as a critical asset for recommendation systems, outlining the necessary steps from understanding business problems and data dimensions to collection, cleaning, integration, and analysis, while distinguishing explicit and implicit user feedback and emphasizing data quality, timeliness, and relevance.

Data CollectionETLRecommendation Systems
0 likes · 11 min read
Data-Driven Foundations for Building Recommendation Systems
JD Retail Technology
JD Retail Technology
Jun 15, 2019 · Artificial Intelligence

Comprehensive 6.18 Preparation: Load Testing, Deep Personalization, and Recommendation Algorithm Optimizations

The department’s extensive 6.18 preparation involved systematic load‑testing, deep learning‑driven personalization of search recommendations, and multiple algorithmic enhancements to improve relevance and conversion, supported by detailed planning, cross‑team coordination, and dedicated night‑shift logistics.

AIRecommendation Systemsalgorithm optimization
0 likes · 6 min read
Comprehensive 6.18 Preparation: Load Testing, Deep Personalization, and Recommendation Algorithm Optimizations
DataFunTalk
DataFunTalk
May 20, 2019 · Artificial Intelligence

Evolution of Alibaba's Advertising CTR Prediction Models: From Linear Methods to Deep Interest Evolution Networks

The article reviews the characteristics of e‑commerce personalized prediction, outlines Alibaba's model iteration from large‑scale linear regression to deep learning architectures such as DIN, CrossMedia, and Deep Interest Evolution, and discusses future directions like disentangled representation and white‑box modeling.

Attention MechanismCTR predictionRecommendation Systems
0 likes · 11 min read
Evolution of Alibaba's Advertising CTR Prediction Models: From Linear Methods to Deep Interest Evolution Networks
DataFunTalk
DataFunTalk
Apr 25, 2019 · Artificial Intelligence

Comparison of Classification and Ranking Models in Recommendation Systems

This article examines the differences and similarities between classification (pointwise) and ranking (pairwise) models for recommendation systems, covering their probabilistic foundations, loss functions, parameter updates, and practical implications such as sensitivity to statistical features and robustness.

Recommendation Systemsclassification modelloss function
0 likes · 10 min read
Comparison of Classification and Ranking Models in Recommendation Systems
Sohu Tech Products
Sohu Tech Products
Apr 17, 2019 · Artificial Intelligence

CTR Estimation in Recommendation Systems: From Logistic Regression to Deep & Cross Networks

This article reviews the evolution of click‑through‑rate (CTR) estimation models for recommendation ranking, covering logistic regression, feature‑engineering tricks, factorization machines, deep neural networks, wide‑and‑deep architectures, and the Deep & Cross Network, while discussing their strengths, limitations, and future research directions.

CTRRecommendation Systemscross network
0 likes · 14 min read
CTR Estimation in Recommendation Systems: From Logistic Regression to Deep & Cross Networks
Hulu Beijing
Hulu Beijing
Apr 10, 2019 · Artificial Intelligence

Designing Deep Learning Models for Item Similarity in Recommendation Systems

This article explains how to build both unsupervised and supervised deep‑learning models that compute item similarity from user behavior, covering prod2vec embeddings, skip‑gram architectures, loss function design, and practical training steps for modern recommender systems.

Collaborative FilteringRecommendation SystemsSupervised Learning
0 likes · 8 min read
Designing Deep Learning Models for Item Similarity in Recommendation Systems
Youku Technology
Youku Technology
Apr 2, 2019 · Artificial Intelligence

How Youku Uses Multimodal AI for Video Understanding, Search, and Recommendation

Youku’s Algorithm Center has built a multimodal AI pipeline that jointly processes visual, audio, and textual signals to enhance video search, recommendation, and digital asset management, overcoming traditional keyword limits, improving relevance and cold‑start issues, while tackling fusion, cost, and interpretability challenges.

Recommendation Systemscontent understandingmedia analytics
0 likes · 15 min read
How Youku Uses Multimodal AI for Video Understanding, Search, and Recommendation
DataFunTalk
DataFunTalk
Mar 19, 2019 · Artificial Intelligence

Using Field-aware FM (FFM) Models for Unified Recall in Recommendation Systems

This article explores how Field-aware Factorization Machines (FFM) can be employed to replace multi‑path recall strategies in industrial recommendation systems, detailing model principles, embedding construction, integration of user, item and context features, performance considerations, and potential for unifying recall and ranking stages.

EmbeddingFFMRecommendation Systems
0 likes · 51 min read
Using Field-aware FM (FFM) Models for Unified Recall in Recommendation Systems
网易UEDC
网易UEDC
Feb 25, 2019 · Product Management

How to Design Effective Content Distribution for Platforms: A LOFTER Case Study

This article examines the core challenges of content distribution on LOFTER, outlines a universal distribution framework based on content, channels, and users, analyzes content organization structures, production controls, value assessment, and channel strategies, and proposes improvements for LOFTER's ecosystem.

Content DistributionLOFTERRecommendation Systems
0 likes · 12 min read
How to Design Effective Content Distribution for Platforms: A LOFTER Case Study
DataFunTalk
DataFunTalk
Feb 20, 2019 · Artificial Intelligence

Recommendation Reasoning and Its Path Toward Future AI

This article explores why recommendation systems need reasoning, how recommendation reasoning connects to future strong AI, discusses explainability, causal inference, graph-based reasoning, and the philosophical underpinnings of AI, while also reflecting on practical examples from Hulu's recommendation platform.

Future AIRecommendation Systemscausal reasoning
0 likes · 25 min read
Recommendation Reasoning and Its Path Toward Future AI
DataFunTalk
DataFunTalk
Feb 13, 2019 · Artificial Intelligence

Reinforcement Learning: Principles, Applications, and the PARL Framework

This comprehensive article explains reinforcement learning fundamentals, compares it with supervised learning, surveys Baidu's industrial RL applications such as recommendation, dialogue, prosthetics, and autonomous driving, introduces the open‑source PARL platform, and discusses current challenges and future research directions.

AIDialogue SystemsPARL
0 likes · 18 min read
Reinforcement Learning: Principles, Applications, and the PARL Framework
Alibaba Cloud Developer
Alibaba Cloud Developer
Feb 13, 2019 · Artificial Intelligence

How Graph Neural Networks are Revolutionizing E‑commerce Recommendations

This article explores how cognitive computing combined with graph neural networks and text generation enables large‑scale interest mining, interpretable embeddings, and multi‑modal recommendation in e‑commerce, outlining platform implementations, explainable methods, and future directions for AI‑driven consumer engagement.

Recommendation Systemscognitive computingexplainable AI
0 likes · 9 min read
How Graph Neural Networks are Revolutionizing E‑commerce Recommendations
DataFunTalk
DataFunTalk
Jan 28, 2019 · Artificial Intelligence

Deep Interest Evolution Network (DIEN): Modeling User Interest Evolution for Click‑Through Rate Prediction

This article introduces the Deep Interest Evolution Network (DIEN), an advanced deep learning model that extracts and evolves user interests over time to improve click‑through rate prediction for display advertising, detailing its background, architecture, auxiliary loss, attention‑augmented GRU, and both offline and online performance gains.

AdvertisingDIENRecommendation Systems
0 likes · 15 min read
Deep Interest Evolution Network (DIEN): Modeling User Interest Evolution for Click‑Through Rate Prediction
Alibaba Cloud Developer
Alibaba Cloud Developer
Jan 9, 2019 · Artificial Intelligence

Master Deep Learning Foundations and 14 Cutting-Edge Recommendation Models

This article introduces core deep‑learning architectures—including MLP, RNN, CNN, auto‑encoders, and RBM—explains common activation and loss functions, and then surveys fourteen influential deep‑learning‑based recommendation algorithms such as FM, wide&deep, deepFM, NCF, GBDT+LR, seq2seq and YouTube DNN, complete with model diagrams and reference links.

AIRecommendation Systemsdeep learning
0 likes · 18 min read
Master Deep Learning Foundations and 14 Cutting-Edge Recommendation Models
Alibaba Cloud Developer
Alibaba Cloud Developer
Jan 8, 2019 · Artificial Intelligence

Unlocking Recommendation Systems: 10 Classic Machine Learning Algorithms Explained

This article surveys ten classic recommendation system algorithms—including collaborative filtering, association rules, Bayesian methods, K‑Nearest Neighbors, decision trees, random forests, matrix factorization, neural networks, word2vec, and logistic regression—detailing their principles, mathematical formulas, and practical implementation steps for real‑world applications.

Collaborative FilteringLogistic RegressionRecommendation Systems
0 likes · 25 min read
Unlocking Recommendation Systems: 10 Classic Machine Learning Algorithms Explained
DataFunTalk
DataFunTalk
Jan 3, 2019 · Artificial Intelligence

Machine Learning and Recommendation System Practice

This article presents a comprehensive overview of applying machine learning to recommendation systems, covering fundamental challenges such as user cold‑start, precise interest modeling, collaborative filtering, and both offline and online evaluation methods, while illustrating concepts with numerous diagrams.

AICollaborative FilteringRecommendation Systems
0 likes · 9 min read
Machine Learning and Recommendation System Practice
Alibaba Cloud Developer
Alibaba Cloud Developer
Dec 28, 2018 · Artificial Intelligence

Elastic Feature Scaling: Boosting Alibaba’s Online Recommendation CTR by 4%

This article describes how Ant Financial’s AI team redesigned TensorFlow to enable elastic feature scaling, introduced a Group‑Lasso optimizer and streaming frequency filtering, compressed models by 90%, and achieved significant CTR and efficiency gains in Alipay’s online recommendation system.

Recommendation SystemsTensorFlowfeature scaling
0 likes · 20 min read
Elastic Feature Scaling: Boosting Alibaba’s Online Recommendation CTR by 4%
Beike Product & Technology
Beike Product & Technology
Dec 6, 2018 · Artificial Intelligence

Recommendation Systems in Real Estate: Practices and Insights from Lianke (Beike) at ArchSummit 2018

This article announces the ArchSummit 2018 global architect summit in Beijing, featuring a talk by Lianke (Beike) recommendation platform expert Xu Yansong on practical recommendation systems in real estate, covering architecture upgrades, algorithm iteration, and lessons learned.

Algorithm IterationArchSummit 2018Recommendation Systems
0 likes · 4 min read
Recommendation Systems in Real Estate: Practices and Insights from Lianke (Beike) at ArchSummit 2018
Alibaba Cloud Developer
Alibaba Cloud Developer
Dec 5, 2018 · Artificial Intelligence

How AI Optimizes E‑Commerce ‘Bundle‑Buy’ with Graph Embedding & Knapsack

This article explains how Alibaba's search team leverages AI techniques such as graph embedding, scenario‑based recommendation, and a multiple‑choice knapsack model to intelligently select complementary items during the Double Eleven shopping festival, balancing price constraints, user experience, and conversion efficiency.

Recommendation Systemse-commercegraph embedding
0 likes · 15 min read
How AI Optimizes E‑Commerce ‘Bundle‑Buy’ with Graph Embedding & Knapsack
Alibaba Cloud Developer
Alibaba Cloud Developer
Dec 4, 2018 · Artificial Intelligence

Unlocking Elastic TensorFlow: Boosting Online Recommendation CTR by 30%

This article presents a comprehensive set of innovations—including elastic feature scaling, a Group Lasso optimizer, streaming frequency filtering, and graph‑cut model compression—that transform TensorFlow for large‑scale online learning, delivering significant CTR gains and up to 90% model size reduction in Alibaba's recommendation systems.

Recommendation Systemsfeature engineeringgroup lasso
0 likes · 19 min read
Unlocking Elastic TensorFlow: Boosting Online Recommendation CTR by 30%
Programmer DD
Programmer DD
Nov 21, 2018 · Artificial Intelligence

What I Learned From My AI Engineer Interview: Recommendation Systems, TF‑IDF, Word2Vec & SVM Explained

A Java developer shares his self‑learning journey into AI, recounts a technical interview covering recommendation system types, TF‑IDF similarity metrics, word2vec behavior modeling, and SVM fundamentals, and reflects on the challenges and resources that helped him transition into algorithm engineering.

AIInterviewRecommendation Systems
0 likes · 7 min read
What I Learned From My AI Engineer Interview: Recommendation Systems, TF‑IDF, Word2Vec & SVM Explained
iQIYI Technical Product Team
iQIYI Technical Product Team
Nov 2, 2018 · Artificial Intelligence

iQIYI Tech Salon Session 3 (Beijing): AI Technology Practices and Applications

The third iQIYI Tech Salon in Beijing, held despite strong winds, showcased five expert talks on AI‑driven video library management, NLP for entertainment content, AI‑based video encoding, traffic anti‑fraud systems, and short‑video personalized recommendation, illustrating AI’s impact on content creation, quality, security, and user experience.

AINLPRecommendation Systems
0 likes · 5 min read
iQIYI Tech Salon Session 3 (Beijing): AI Technology Practices and Applications
Hulu Beijing
Hulu Beijing
Oct 12, 2018 · Artificial Intelligence

How Hulu Boosted Recommendation Diversity with Determinantal Point Processes

This article explains how Hulu tackled the trade‑off between accuracy and diversity in its massive video recommendation system by applying Determinantal Point Processes and an efficient incremental greedy algorithm, achieving 100× speed‑ups without sacrificing recommendation quality.

DiversityHuluRecommendation Systems
0 likes · 7 min read
How Hulu Boosted Recommendation Diversity with Determinantal Point Processes
DataFunTalk
DataFunTalk
Oct 12, 2018 · Artificial Intelligence

Market Mechanisms and Control Measures in Ele.me Food Delivery Recommendation Algorithms

The article presents a comprehensive overview of Ele.me's food‑delivery recommendation system, detailing its business model, platform goals, unique challenges, market‑driven efficiency mechanisms, control strategies, system architecture, model evolution, and online‑learning techniques used to balance short‑term performance with long‑term ecosystem health.

AIEle.meRanking
0 likes · 15 min read
Market Mechanisms and Control Measures in Ele.me Food Delivery Recommendation Algorithms
21CTO
21CTO
Sep 24, 2018 · Artificial Intelligence

Why Recommendation Algorithms Aren’t Magic: A Practical Guide

This article explains the fundamentals of recommendation algorithms, illustrates their modest impact with real‑world examples, and outlines how modern e‑commerce systems collect data, rank items, and use rapid A/B testing to continuously improve personalized recommendations.

A/B testingRecommendation Systemsalgorithm design
0 likes · 10 min read
Why Recommendation Algorithms Aren’t Magic: A Practical Guide
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 29, 2018 · Artificial Intelligence

How Graph Embedding Boosts E‑Commerce Recommendations: GES & EGES Explained

An in‑depth look at Alibaba’s billion‑scale graph embedding framework—GES and EGES—reveals how side‑information‑enhanced embeddings address user long‑tail coverage and cold‑start challenges, improving recommendation diversity and discovery across massive e‑commerce datasets and enabling real‑time personalized ranking.

Recommendation Systemscold starte-commerce
0 likes · 7 min read
How Graph Embedding Boosts E‑Commerce Recommendations: GES & EGES Explained
AntTech
AntTech
Aug 22, 2018 · Artificial Intelligence

Ant Financial’s KDD 2018 Papers: Graph-Based Fraud Detection, GeniePath GNN, and Distributed Collaborative Hashing

The article presents three Ant Financial research papers featured at KDD 2018—one on graph‑learning fraud detection for return‑freight insurance, another introducing the adaptive GeniePath graph neural network, and a third describing a distributed collaborative hashing system for large‑scale recommendation—highlighting their methodologies, experimental results, and practical impact on Ant Financial’s services.

Ant FinancialHashingRecommendation Systems
0 likes · 21 min read
Ant Financial’s KDD 2018 Papers: Graph-Based Fraud Detection, GeniePath GNN, and Distributed Collaborative Hashing
360 Quality & Efficiency
360 Quality & Efficiency
Jun 4, 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—describes their typical application scenarios, and explores various testing methods and evaluation metrics such as offline experiments, user surveys, A/B testing, and performance indicators like accuracy, coverage, and robustness.

OptimizationRecommendation Systemsalgorithm testing
0 likes · 12 min read
Common Engineering Algorithms and Their Testing Methods
360 Quality & Efficiency
360 Quality & Efficiency
Jun 4, 2018 · Artificial Intelligence

How to Conduct Algorithm Testing in Engineering Projects

This article outlines the challenges of algorithm testing in real‑world engineering, proposes a step‑by‑step testing framework—from understanding business context and verifying data exchanges to evaluating performance metrics and iterating improvements—while offering practical advice and examples.

A/B testingRecommendation SystemsSoftware Engineering
0 likes · 7 min read
How to Conduct Algorithm Testing in Engineering Projects
Hulu Beijing
Hulu Beijing
May 31, 2018 · Artificial Intelligence

How AI is Transforming Video Streaming: Today’s Practices and Future Trends

In this talk, Hulu’s Zhuge Yue explains how massive user data, diverse content, and advanced AI and machine learning techniques power personalized recommendations, content embedding, explainable AI, and innovative ad integration, outlining current implementations and future architectural directions for video streaming platforms.

AIRecommendation Systemscontent embedding
0 likes · 18 min read
How AI is Transforming Video Streaming: Today’s Practices and Future Trends
Meitu Technology
Meitu Technology
May 23, 2018 · Artificial Intelligence

Machine Learning and Optimization Problems: Applications and Exploration

Meitu Technology’s technical salon on June 9, 2018 in Xiamen showcased how its AI‑driven deep ranking, video‑clustering, and data‑structure‑based optimization techniques improve personalization, recommendation and economic‑focused problem solving for billions of mobile users, targeting mid‑senior R&D and algorithm engineers.

Data StructuresOptimizationRecommendation Systems
0 likes · 6 min read
Machine Learning and Optimization Problems: Applications and Exploration
Tencent Cloud Developer
Tencent Cloud Developer
May 9, 2018 · Artificial Intelligence

From Mathematics to Machine Learning: A Personal Journey Through Recommendation, Security, and AIOps

A mathematician‑turned‑engineer recounts his 2015‑2022 path from undocumented recommendation systems at Tencent, through high‑precision security models, reinforcement‑learning game AI, quantum‑ML studies, to large‑scale AIOps time‑series anomaly detection, offering practical lessons for anyone transitioning into machine learning.

AIOpsLogistic RegressionRecommendation Systems
0 likes · 16 min read
From Mathematics to Machine Learning: A Personal Journey Through Recommendation, Security, and AIOps
Alibaba Cloud Developer
Alibaba Cloud Developer
Apr 26, 2018 · Artificial Intelligence

How TensorFlowRS Supercharges Large‑Scale Search & Recommendation with 10×‑100× Speedups

This article describes TensorFlowRS, an Alibaba‑built extension of TensorFlow that tackles the massive compute and sparse‑feature challenges of search, advertising and recommendation by redesigning the parameter server, adding fail‑over, gradient‑compensation, online‑learning support, advanced training modes and visualisation, achieving up to 100× training speedup and improved model quality.

Distributed TrainingRecommendation SystemsTensorFlow
0 likes · 16 min read
How TensorFlowRS Supercharges Large‑Scale Search & Recommendation with 10×‑100× Speedups
Java Backend Technology
Java Backend Technology
Apr 20, 2018 · Artificial Intelligence

How Do Modern Recommendation Systems Balance Accuracy, Diversity, and Surprise?

This article explains the objectives, methods, architecture, and key algorithms of modern recommendation systems, covering popular, manual, related, and personalized approaches, the data pipeline, real‑time challenges, cold‑start handling, diversity, content quality, and exploration‑exploitation strategies.

Collaborative FilteringRecommendation Systemscontent-based filtering
0 likes · 15 min read
How Do Modern Recommendation Systems Balance Accuracy, Diversity, and Surprise?
Efficient Ops
Efficient Ops
Apr 17, 2018 · Artificial Intelligence

From Math to ML: My Path Through Recommendation, Security, and AIOps

This article chronicles the author’s transition from a mathematics background to machine learning, detailing early challenges, hands‑on projects in recommendation systems, security, and AIOps, and sharing practical insights on feature engineering, model evaluation, and large‑scale anomaly detection.

AIOpsRecommendation Systemsanomaly detection
0 likes · 17 min read
From Math to ML: My Path Through Recommendation, Security, and AIOps
Meituan Technology Team
Meituan Technology Team
Mar 29, 2018 · Artificial Intelligence

Deep Learning Model Applications and Optimizations for Recommendation Ranking at Meituan

The paper describes how Meituan tackles information overload on its lifestyle platform by training multi‑task deep neural networks on billions of interaction logs using a distributed PS‑Lite framework, employing sophisticated feature engineering, missing‑value imputation, KL‑regularization and Neural Factorization Machines to boost offline AUC and online CTR in the “Guess You Like” recommendation feed, while introducing training‑time optimizations and outlining future multi‑task and contextual enhancements.

Recommendation Systemsdeep learningfeature engineering
0 likes · 16 min read
Deep Learning Model Applications and Optimizations for Recommendation Ranking at Meituan
Tencent Cloud Developer
Tencent Cloud Developer
Mar 16, 2018 · Artificial Intelligence

Pairwise Ranking Factorization Machines (PRFM) for Feed Recommendation in Tencent Shield

The article presents Pairwise Ranking Factorization Machines (PRFM), a pairwise‑learning extension of Factorization Machines that replaces Tencent Shield’s pointwise binary‑classification pipeline, generates user‑item‑item triples, optimizes a cross‑entropy loss, and achieves about a 5% relative UV click‑through gain on the HandQ anime feed while outlining offline metrics, hyper‑parameter tuning, and future informed‑sampling enhancements.

RankingRecommendation Systemsfactorization machines
0 likes · 10 min read
Pairwise Ranking Factorization Machines (PRFM) for Feed Recommendation in Tencent Shield
21CTO
21CTO
Feb 24, 2018 · Artificial Intelligence

Why Deep Learning Is Revolutionizing Recommendation Systems

This article explores how deep learning techniques such as item embeddings, autoencoders, Word2Vec, and session‑based neural models are applied to recommendation systems, highlighting their advantages, key architectures, and recent advances from industry and research.

AIRecommendation Systemsautoencoders
0 likes · 17 min read
Why Deep Learning Is Revolutionizing Recommendation Systems
Architecture Digest
Architecture Digest
Feb 22, 2018 · Artificial Intelligence

Deep Learning Applications in Recommendation Systems

This article explains why deep learning has become essential for modern recommendation systems, describing its advantages such as automatic feature extraction, noise robustness, sequential modeling with RNNs, and improved user‑item representation, and reviews major deep‑learning‑based recommendation models and techniques.

Recommendation SystemsWord2Vecautoencoders
0 likes · 17 min read
Deep Learning Applications in Recommendation Systems
21CTO
21CTO
Jan 18, 2018 · Artificial Intelligence

How Ctrip Scales Personalized Travel Recommendations: From Recall to Ranking

This article details Ctrip's end‑to‑end personalized recommendation system for travel, covering data collection, candidate recall methods, ranking models, feature engineering practices, and future directions, illustrating how millions of users receive tailored travel suggestions.

CtripRecommendation SystemsTravel
0 likes · 17 min read
How Ctrip Scales Personalized Travel Recommendations: From Recall to Ranking
21CTO
21CTO
Dec 17, 2017 · Artificial Intelligence

How Collaborative Filtering Turns User Behavior into Smart Recommendations

This article explains the fundamentals of collaborative filtering, detailing explicit and implicit user feedback, power‑law behavior patterns, neighborhood‑based and latent‑factor recommendation algorithms, and how they are applied in e‑commerce and social platforms.

AICollaborative FilteringRecommendation Systems
0 likes · 8 min read
How Collaborative Filtering Turns User Behavior into Smart Recommendations
Architecture Digest
Architecture Digest
Dec 17, 2017 · Artificial Intelligence

Introduction to User Behavior and Collaborative Filtering in Recommendation Systems

This article explains user behavior concepts and feedback types, introduces collaborative filtering methods including user‑based, item‑based and latent factor models, discusses similarity measures, power‑law distributions, and practical considerations such as negative sampling, providing a comprehensive overview for building recommendation systems.

Collaborative FilteringRecommendation SystemsUser Behavior
0 likes · 9 min read
Introduction to User Behavior and Collaborative Filtering in Recommendation Systems
Baixing.com Technical Team
Baixing.com Technical Team
Nov 30, 2017 · Artificial Intelligence

How User Profiling Powers Modern Recommendation Systems

This article explains what user profiling is, why it’s crucial for recommendation systems, outlines key dimensions such as personal attributes, status, and interests, describes algorithms like classification and autoregressive models, and details offline and real‑time computation methods, evaluation techniques, and practical examples.

AlgorithmRecommendation Systemsdata mining
0 likes · 11 min read
How User Profiling Powers Modern Recommendation Systems
Baixing.com Technical Team
Baixing.com Technical Team
Nov 29, 2017 · Artificial Intelligence

How Content Features Power Modern Recommendation Systems

Content features transform unstructured entities like articles, images, and videos into structured descriptors—such as categories, tags, and keywords—enabling precise search recall, personalized recommendations, and effective labeling through methods like classification, convergent tags, keyword extraction, and both manual and automated annotation.

Recommendation Systemsautomatic annotationcontent features
0 likes · 12 min read
How Content Features Power Modern Recommendation Systems
Meituan Technology Team
Meituan Technology Team
Nov 23, 2017 · Artificial Intelligence

O2O Machine Learning Applications Seminar

The O2O Machine Learning Applications Seminar, featuring experts from Meituan‑Dianping and Alibaba, explores real‑world ML implementations for online‑to‑offline services, including online learning for search, Alibaba’s Ali Xiaomi intelligent assistant, deep‑learning‑driven recommendation systems, and advertising algorithms such as CTR and CVR optimization, sharing practical insights and best practices.

Artificial IntelligenceO2ORecommendation Systems
0 likes · 5 min read
O2O Machine Learning Applications Seminar
iQIYI Technical Product Team
iQIYI Technical Product Team
Nov 10, 2017 · Artificial Intelligence

iQIYI Recommendation System: Architecture, Model Evolution, and Ranking Strategies

The iQIYI recommendation system combines a two‑stage pipeline of recall and ranking, evolving from logistic regression to a GBDT‑FM‑DNN ensemble, using online feature storage, extensive feature engineering, and configurable strategies to deliver personalized video suggestions while addressing feature drift and multi‑objective business goals.

GBDTRankingRecommendation Systems
0 likes · 13 min read
iQIYI Recommendation System: Architecture, Model Evolution, and Ranking Strategies
Meituan Technology Team
Meituan Technology Team
Oct 12, 2017 · Artificial Intelligence

Machine Learning Q&A: Data Imputation, Feature Selection, Recommendation Systems and More

The article answers ten machine‑learning questions, explaining how to impute missing behavior data, extract and select features, describe Meituan‑Dianping’s recommendation pipeline, suggest a beginner learning path, clarify L1 sparsity, recommend TextCNN for text, discuss search‑ranking sample bias, label generation for wide‑deep models, the shift to deep‑learning video detection, and the use of factorization machines for CTR with open‑source examples.

L1 RegularizationRecommendation SystemsText classification
0 likes · 7 min read
Machine Learning Q&A: Data Imputation, Feature Selection, Recommendation Systems and More
21CTO
21CTO
Sep 27, 2017 · Artificial Intelligence

How Tagging and User Profiling Power Modern Recommendation Systems

This article explores how simple tagging and user profiling underpin modern recommendation systems, contrasting tag‑based, flexible representations with traditional hierarchical classifications, and examines practical applications such as personalized advertising, industry research, and product optimization.

Recommendation Systemsdata miningpersonalization
0 likes · 13 min read
How Tagging and User Profiling Power Modern Recommendation Systems
iQIYI Technical Product Team
iQIYI Technical Product Team
Sep 22, 2017 · Artificial Intelligence

iQIYI NLP Team: Research Topics, Progress, and Applications in Video Services

The iQIYI NLP team applies lexical analysis, knowledge‑graph construction, tag recommendation, query understanding, voice‑assistant semantics, sentiment mining, and box‑office/view‑count prediction—leveraging weakly labeled data, CRF/CNN‑CRF models and deep learning—to enhance video comprehension, recommendation, search and commercial services across the platform.

NLPRecommendation SystemsSpeech Assistant
0 likes · 13 min read
iQIYI NLP Team: Research Topics, Progress, and Applications in Video Services
21CTO
21CTO
Sep 15, 2017 · Artificial Intelligence

Mastering Recommendation Systems: Goals, Algorithms, and Real-World Practices

This article explains the objectives of recommendation systems, outlines four recommendation approaches, dives into personalized recommendation architecture and core algorithms, and discusses practical challenges such as real‑time processing, cold‑start, diversity, content quality, and exploration‑exploitation trade‑offs.

Collaborative FilteringRecommendation Systemscold start
0 likes · 16 min read
Mastering Recommendation Systems: Goals, Algorithms, and Real-World Practices
21CTO
21CTO
Aug 17, 2017 · Artificial Intelligence

How Alibaba’s Deep Interest Network Powers Personalized Shopping for 400 Million Users

Alibaba’s Vice President Gu XueMei explained at the 40th ACM SIGIR conference how deep interest networks, driven by big data and large‑scale deep learning, enable highly personalized e‑commerce experiences that dramatically reduce user churn and boost click‑through rates.

Recommendation Systemse-commercepersonalization
0 likes · 5 min read
How Alibaba’s Deep Interest Network Powers Personalized Shopping for 400 Million Users
21CTO
21CTO
Aug 4, 2017 · Artificial Intelligence

AI Behind Hulu's Video Recommendations: From Collaborative Filtering to Neural Nets

In this talk, Hulu’s research director Zhou Hanning explains the key factors influencing recommendation system performance, describes optimization goals, explores collaborative filtering, matrix factorization, and neural‑network approaches—including metadata‑driven transfer learning and cold‑start solutions for live streaming—and shares practical AI implementations that improve user experience and engagement.

AIMetadataRecommendation Systems
0 likes · 10 min read
AI Behind Hulu's Video Recommendations: From Collaborative Filtering to Neural Nets
21CTO
21CTO
Jul 8, 2017 · Artificial Intelligence

Mastering Recommendation Systems: From Collaborative Filtering to Deep Learning

This article surveys major recommendation system techniques—from collaborative filtering and matrix factorization to clustering and deep‑learning approaches like YouTube’s two‑stage neural network—explaining their principles, strengths, and practical considerations for building effective personalized recommenders.

ClusteringCollaborative FilteringRecommendation Systems
0 likes · 10 min read
Mastering Recommendation Systems: From Collaborative Filtering to Deep Learning
21CTO
21CTO
Apr 20, 2017 · Artificial Intelligence

How Facebook Evaluates Its Newsfeed Recommendations: Metrics, Models, and User Surveys

Facebook evaluates its Newsfeed recommendation quality through three pillars—machine-learning model metrics like AUC, extensive product data KPIs such as DAU and interaction rates, and user-survey feedback—while maintaining long-term backtests and emphasizing the risks of relying on a single metric.

A/B testingKPIRecommendation Systems
0 likes · 7 min read
How Facebook Evaluates Its Newsfeed Recommendations: Metrics, Models, and User Surveys
21CTO
21CTO
Apr 1, 2017 · Artificial Intelligence

How Modern Apps Use AI to Personalize Your Content Feed

The article explores how recommendation technologies powered by machine learning permeate everyday platforms—from e‑commerce and video services to social media and news apps—detailing the data they collect, the algorithms they employ, and the limits of personalization in unpredictable human scenarios.

Recommendation Systemscontent filteringmachine learning
0 likes · 7 min read
How Modern Apps Use AI to Personalize Your Content Feed
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 8, 2017 · Artificial Intelligence

How Private History Can Supercharge E‑commerce Recommendations: The PH‑MAB Mechanism Explained

This article introduces the PH‑MAB mechanism that combines public and private transaction histories to improve multi‑armed bandit‑based recommendation systems, explains its truthful mechanism‑design foundation, and shows how it reduces regret and boosts platform revenue compared to traditional epsilon‑greedy approaches.

Recommendation Systemse-commercemechanism design
0 likes · 6 min read
How Private History Can Supercharge E‑commerce Recommendations: The PH‑MAB Mechanism Explained
21CTO
21CTO
Mar 2, 2017 · Artificial Intelligence

How User Personas Power Modern Recommendation Systems: From Theory to NetEase Yanxuan

This article explains the concept and construction of user personas, explores the essence and algorithms of recommendation systems, compares movie and e‑commerce scenarios, and details NetEase Yanxuan's practical CTR‑based recommendation model with extensive feature engineering.

Recommendation Systemse-commercefeature engineering
0 likes · 13 min read
How User Personas Power Modern Recommendation Systems: From Theory to NetEase Yanxuan
Alibaba Cloud Developer
Alibaba Cloud Developer
Feb 24, 2017 · Artificial Intelligence

How Reinforcement Learning Transforms E‑Commerce Search and Recommendation

This article explores how Taobao leverages reinforcement learning, multi‑armed bandits, and reward‑shaping techniques to improve large‑scale e‑commerce search ranking and recommendation, detailing problem modeling, algorithm designs such as Tabular Q‑learning and DDPG, experimental results from Double‑11, and advanced models like GBDT+FTRL and Wide‑&‑Deep.

Bandit AlgorithmsRecommendation Systemsdeep learning
0 likes · 19 min read
How Reinforcement Learning Transforms E‑Commerce Search and Recommendation
Ctrip Technology
Ctrip Technology
Feb 23, 2017 · Artificial Intelligence

Report on AAAI‑2017 Conference Highlights and Ctrip’s Hybrid Collaborative Filtering Model

The article recounts the author’s experience at AAAI‑2017 in San Francisco, summarizes key talks, panels and award‑winning papers, and details Ctrip’s hybrid collaborative‑filtering model with a stacked denoising auto‑encoder that improves recommendation performance and addresses data sparsity.

AAAI-2017Artificial IntelligenceCtrip
0 likes · 9 min read
Report on AAAI‑2017 Conference Highlights and Ctrip’s Hybrid Collaborative Filtering Model
Alibaba Cloud Developer
Alibaba Cloud Developer
Feb 22, 2017 · Artificial Intelligence

How Alibaba’s AI Powers Real‑Time Customer Segmentation and Personalized Shopping

This article explains how Alibaba leverages AI, big‑data analytics, and advanced recommendation algorithms to enable real‑time visitor clustering, personalized storefronts, and tailored content across its Customer Operation Platform, Double 11 promotion pages, QianNiu headlines, and service market, delivering significant conversion and engagement gains.

AIBig DataRecommendation Systems
0 likes · 18 min read
How Alibaba’s AI Powers Real‑Time Customer Segmentation and Personalized Shopping
Alibaba Cloud Developer
Alibaba Cloud Developer
Feb 16, 2017 · Artificial Intelligence

How Reinforcement Learning Transforms E‑Commerce Search and Recommendation at Scale

This article explores how Alibaba's Taobao leverages reinforcement learning, Markov decision processes, and reward shaping to improve large‑scale product search ranking and recommendation, detailing problem modeling, algorithm designs such as Tabular Q‑learning and DDPG, experimental results, and advanced recommendation models like GBDT‑FTRL and Wide‑Deep.

MDPRecommendation Systemsdeep learning
0 likes · 21 min read
How Reinforcement Learning Transforms E‑Commerce Search and Recommendation at Scale
Architects Research Society
Architects Research Society
Nov 21, 2016 · Artificial Intelligence

Data Science Q&A: Overfitting, Experimental Design, Tall/Wide Data, Chart Junk, Outliers, Extreme Value Theory, Recommendation Engines, and Visualization

This article presents a series of data‑science questions and expert answers covering overfitting, experimental design for user behavior, the distinction between tall and wide data, detecting chart junk, outlier detection methods, extreme‑value theory for rare events, recommendation‑engine fundamentals, and techniques for visualizing high‑dimensional data.

Experimental DesignRecommendation Systemschart junk
0 likes · 18 min read
Data Science Q&A: Overfitting, Experimental Design, Tall/Wide Data, Chart Junk, Outliers, Extreme Value Theory, Recommendation Engines, and Visualization
StarRing Big Data Open Lab
StarRing Big Data Open Lab
Nov 4, 2016 · Artificial Intelligence

How Item Features Power Music Recommendations: A Hands‑On Guide

This article explains how recommendation systems can use item‑level features instead of user ratings, illustrating the approach with Pandora's music‑gene project, detailing feature selection, scoring, distance calculations, standardization, and classification techniques across music, athlete, Iris, and automobile datasets.

Recommendation SystemsStandardizationclassification
0 likes · 20 min read
How Item Features Power Music Recommendations: A Hands‑On Guide
StarRing Big Data Open Lab
StarRing Big Data Open Lab
Oct 20, 2016 · Artificial Intelligence

How Collaborative Filtering Powers Recommendations: From Manhattan to Cosine Similarity

This article walks through the fundamentals of recommendation systems, explaining collaborative filtering and various similarity measures—including Manhattan, Euclidean, Minkowski, Pearson correlation, and cosine similarity—while discussing their suitability for dense, sparse, or biased rating data and introducing K‑Nearest Neighbors for practical implementation.

Collaborative FilteringRecommendation Systemsdata mining
0 likes · 15 min read
How Collaborative Filtering Powers Recommendations: From Manhattan to Cosine Similarity
Alibaba Cloud Developer
Alibaba Cloud Developer
Sep 13, 2016 · Artificial Intelligence

How Game Theory and AI Stop Fake Reviews on E‑Commerce Platforms

This article explains how Alibaba combines big‑data analytics, machine learning, and mechanism‑design game theory to create a recommendation system that removes incentives for merchants to generate fake orders, improving fairness and user experience on e‑commerce platforms.

Game TheoryRecommendation Systemsanti-fraud
0 likes · 3 min read
How Game Theory and AI Stop Fake Reviews on E‑Commerce Platforms
ITPUB
ITPUB
Aug 31, 2016 · Artificial Intelligence

How Recommendation Systems Evolve: From Algorithms to Architecture Mastery

This talk traces the evolution of recommendation systems from early algorithm‑centric prototypes through a wild‑growth phase to a mature, architecture‑driven design, highlighting practical challenges, design principles, and lessons learned for building scalable, maintainable recommendation platforms.

AIRecommendation Systemsarchitecture
0 likes · 19 min read
How Recommendation Systems Evolve: From Algorithms to Architecture Mastery
Hujiang Technology
Hujiang Technology
Jul 27, 2016 · Big Data

Hujiang Technology Salon: Data Applications – Summaries of Five Expert Talks

On July 23, 2016, Hujiang hosted a technology salon focused on data applications, featuring five expert presentations covering data-driven operations in online education, O2O logistics, e‑commerce recommendation systems, pitfalls in personalization, and deep‑learning‑based image search, accompanied by case studies and visual materials.

Data AnalyticsRecommendation Systemsdata-driven operations
0 likes · 4 min read
Hujiang Technology Salon: Data Applications – Summaries of Five Expert Talks
Ctrip Technology
Ctrip Technology
Jul 9, 2016 · Artificial Intelligence

Highlights from Ctrip Technology Center Deep Learning Meetup in Shanghai

The Ctrip Technology Center hosted a deep learning meetup in Shanghai featuring academic and industry experts who presented applications of AI in tourism, advertising, natural language processing, computer vision, knowledge graphs, recommendation systems, and discussed future research directions.

Artificial IntelligenceRecommendation SystemsShanghai
0 likes · 7 min read
Highlights from Ctrip Technology Center Deep Learning Meetup in Shanghai
Hulu Beijing
Hulu Beijing
Jun 23, 2016 · Artificial Intelligence

How Hulu’s Neural Autoregressive Model Revolutionized Collaborative Filtering at ICML 2016

At ICML 2016 in New York, Hulu’s research team presented their paper ‘A Neural Autoregressive Approach to Collaborative Filtering,’ showcasing a deep‑learning model that outperformed existing methods on benchmark datasets like Netflix, highlighting Hulu’s emerging leadership in recommendation algorithms.

Collaborative FilteringICML 2016Recommendation Systems
0 likes · 3 min read
How Hulu’s Neural Autoregressive Model Revolutionized Collaborative Filtering at ICML 2016
Hulu Beijing
Hulu Beijing
Apr 27, 2016 · Artificial Intelligence

How CF-NADE Revolutionizes Collaborative Filtering with Neural Autoregression

The article highlights Hulu’s award‑winning paper on a neural autoregressive approach to collaborative filtering, detailing its acceptance at ICML 2016, the authors’ expertise, and how the CF‑NADE model outperforms existing methods on major recommendation datasets.

Collaborative FilteringICMLRecommendation Systems
0 likes · 4 min read
How CF-NADE Revolutionizes Collaborative Filtering with Neural Autoregression
Architecture Digest
Architecture Digest
Apr 22, 2016 · Artificial Intelligence

An Introductory Overview of Recommendation Systems and Their Core Algorithms

This article introduces the basic concepts, purposes, and a range of algorithms—including popularity‑based, collaborative filtering, content‑based, model‑based, and hybrid methods—used in recommendation systems, and discusses evaluation metrics and improvement strategies for practical deployment.

AICollaborative FilteringRecommendation Systems
0 likes · 15 min read
An Introductory Overview of Recommendation Systems and Their Core Algorithms
21CTO
21CTO
Jan 6, 2016 · Artificial Intelligence

From Naïve Algorithms to Scalable Recommendations: Jiayuan’s Journey

This article chronicles the evolution of Jiayuan’s dating recommendation system from early item‑based kNN experiments through a feature‑engineering focused engineering year and a product‑oriented optimization phase, while also reviewing several advanced machine‑learning techniques the author explored.

Logistic RegressionRecommendation Systemsfeature engineering
0 likes · 15 min read
From Naïve Algorithms to Scalable Recommendations: Jiayuan’s Journey
21CTO
21CTO
Jan 4, 2016 · Artificial Intelligence

Understanding Core Recommendation Techniques: Content, Collaborative, and Hybrid Methods

This article surveys the main recommendation approaches—including content‑based, collaborative filtering, association‑rule, utility‑based, knowledge‑based, and hybrid methods—detailing their principles, advantages, drawbacks, and typical combination strategies for building effective recommender systems.

Artificial IntelligenceCollaborative FilteringRecommendation Systems
0 likes · 10 min read
Understanding Core Recommendation Techniques: Content, Collaborative, and Hybrid Methods
Architects Research Society
Architects Research Society
Dec 12, 2015 · Artificial Intelligence

Personalized Recommendation Best Practices

This article explains the fundamentals and business value of personalized recommendation systems for e‑commerce, outlines practical implementations on homepages, list pages, and search result pages, and provides case studies showing how tailored product suggestions improve conversion rates, user experience, and sales performance.

AIRecommendation Systemse-commerce
0 likes · 11 min read
Personalized Recommendation Best Practices
21CTO
21CTO
Sep 1, 2015 · Artificial Intelligence

How the NYT Revamped Its Recommendation Engine with Collaborative Topic Modeling

This article explains how the New York Times redesigned its "Recommended for You" system by combining content‑based filtering, collaborative filtering, and a collaborative topic‑modeling approach that uses LDA, reader‑signal adjustments, and fast preference calculations to deliver personalized article suggestions.

Collaborative FilteringLDARecommendation Systems
0 likes · 12 min read
How the NYT Revamped Its Recommendation Engine with Collaborative Topic Modeling
21CTO
21CTO
Aug 21, 2015 · Artificial Intelligence

How Facebook Scales Recommendations with Distributed Machine Learning and Giraph

This article explains how Facebook tackles massive recommendation data—over 100 billion ratings—by using distributed collaborative filtering, matrix factorization, SGD/ALS hybrid algorithms, and a novel work‑to‑work communication scheme built on Apache Giraph to achieve high performance and scalability.

ALSApache GiraphCollaborative Filtering
0 likes · 9 min read
How Facebook Scales Recommendations with Distributed Machine Learning and Giraph