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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.

Evaluation MetricsRecommendation 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 testingMetricsRecommendation Systems
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 SystemsVideo Streaming
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 StructuresRecommendation Systemsmachine learning
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.

Recommendation SystemsSQLaiops
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 TrainingOnline LearningParameter Server
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.

Real-time ProcessingRecommendation Systemscollaborative 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.

Recommendation Systemsaiopsanomaly 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.

Deep LearningRecommendation Systemsfeature 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.

Recommendation Systemsfactorization machinespairwise learning
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.

AIDeep LearningRecommendation Systems
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.

Deep LearningRecommendation SystemsWord2Vec
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.

AIRecommendation Systemscollaborative filtering
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.

Recommendation Systemscollaborative filteringlatent factor model
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.

Recommendation Systemsalgorithmdata 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 SystemsTaggingautomatic annotation
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.

Deep LearningO2OOnline Learning
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.

GBDTRecommendation Systemsdeep neural networks
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.

Deep LearningL1 RegularizationRecommendation Systems
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 SystemsTaggingdata mining
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.

Real-TimeRecommendation 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.

AIRecommendation SystemsVideo Streaming
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.

Deep LearningRecommendation SystemsYouTube
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 AlgorithmsDeep LearningRecommendation Systems
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-2017CtripDeep Learning
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.

Deep LearningMDPRecommendation Systems
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.

Extreme Value TheoryRecommendation 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 Systemsclassificationdistance metrics
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.

Recommendation Systemscollaborative filteringdata 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 AnalyticsDeep LearningRecommendation Systems
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.

Deep LearningRecommendation 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.

ICML 2016Recommendation Systemscollaborative filtering
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.

ICMLRecommendation Systemscollaborative filtering
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.

AIRecommendation Systemscollaborative filtering
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.

Recommendation Systemsfeature engineeringlogistic regression
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.

Recommendation Systemsartificial intelligencecollaborative filtering
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 SystemsUser experience
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.

LDARecommendation Systemscollaborative filtering
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 GiraphFacebook
0 likes · 9 min read
How Facebook Scales Recommendations with Distributed Machine Learning and Giraph