matrix factorization

NetEase LeiHuo UX Big Data Technology

Aug 23, 2023 · Artificial Intelligence

Model-Based Collaborative Filtering Algorithms for Game Item Recommendation

This article explains the principles of collaborative filtering, outlines its three main types—user‑based, item‑based, and model‑based—and focuses on model‑based approaches such as matrix factorization, clustering, and deep‑learning techniques for recommending personalized game items to improve player experience and monetization.

Recommendation systemsartificial intelligencecollaborative filtering

0 likes · 7 min read

Model-Based Collaborative Filtering Algorithms for Game Item Recommendation

Model Perspective

Oct 13, 2022 · Artificial Intelligence

How BPR Transforms Recommendation Ranking: A Deep Dive

The article introduces the Bayesian Personalized Ranking (BPR) algorithm, explains its background in ranking‑based recommendation, details its probabilistic modeling assumptions, optimization objective, gradient‑based learning process, and compares it with matrix‑factorization methods like FunkSVD, providing a concise training workflow.

BPRRankingRecommendation systems

0 likes · 8 min read

How BPR Transforms Recommendation Ranking: A Deep Dive

Architecture Digest

Aug 27, 2022 · Artificial Intelligence

Understanding Collaborative Filtering, Matrix Factorization, and Spark ALS for Recommendation Systems

This article explains the fundamentals of recommendation systems, introduces collaborative filtering (both user‑based and item‑based), derives the matrix‑factorization model with ALS optimization, provides a complete Python implementation, and demonstrates how to apply Spark ALS in both demo and production environments.

ALSSparkcollaborative filtering

0 likes · 29 min read

Understanding Collaborative Filtering, Matrix Factorization, and Spark ALS for Recommendation Systems

vivo Internet Technology

Jul 20, 2022 · Artificial Intelligence

Collaborative Filtering and Matrix Factorization: Theory and Spark ALS Implementation

The article introduces collaborative filtering, derives the matrix‑factorization model R≈X·Yᵀ with L2‑regularized ALS updates, demonstrates a full Python example on a small rating matrix, then shows how to implement and scale Spark’s ALS for massive user‑item data, ending with production tips and references.

ALSMachine LearningRecommendation systems

0 likes · 25 min read

Collaborative Filtering and Matrix Factorization: Theory and Spark ALS Implementation

DeWu Technology

Jan 18, 2021 · Artificial Intelligence

Recall Stage in Recommendation Systems: From Intuition to Deep Learning

The recall stage, the first filtering step after candidate generation, transforms intuitive attribute‑based shortcuts into sophisticated matrix‑factorization and embedding methods—such as dual‑tower and tree‑based models—enabling fast, personalized, diverse candidate selection for real‑time recommendation pipelines.

Recommendation systemscollaborative filteringdeep learning

0 likes · 13 min read

Recall Stage in Recommendation Systems: From Intuition to Deep Learning

DataFunTalk

Dec 1, 2020 · Artificial Intelligence

A Comprehensive Overview of Embedding Techniques for Recommendation Systems

This article systematically reviews mainstream embedding technologies—including matrix factorization, static and dynamic word embeddings, and graph‑based methods—explaining their principles, implementations, and practical applications in recommendation, advertising, and search systems.

Graph Neural NetworksNatural Language ProcessingRecommendation systems

0 likes · 32 min read

A Comprehensive Overview of Embedding Techniques for Recommendation Systems

DataFunTalk

Oct 18, 2020 · Artificial Intelligence

Unifying Skip‑gram and Matrix Factorization for Graph Embedding and Enhancing It with Sparse Matrix Techniques

This article reviews how skip‑gram‑based graph embedding methods such as DeepWalk, LINE and node2vec can be interpreted as matrix factorization, explains the NetMF and NetSMF frameworks that use sparse matrix approximations and random SVD for large‑scale networks, and discusses extensions like GATNE and deep clustering approaches to address practical challenges in constructing and applying graph representations.

Graph Neural Networksgraph embeddingmatrix factorization

0 likes · 13 min read

Unifying Skip‑gram and Matrix Factorization for Graph Embedding and Enhancing It with Sparse Matrix Techniques

DataFunTalk

Jul 12, 2020 · Artificial Intelligence

Social Tagging and Folksonomy in Recommendation Systems: Models, Algorithms, and Applications

This article surveys the role of social tagging (folksonomy) in modern recommendation systems, describing how user‑generated tags form a three‑dimensional "tag cube" that can be combined with rating matrices, and reviewing a range of algorithms—including neighbor‑based, ranking (FolkRank/SocialRank), content‑based, linear regression, and matrix‑factorization approaches—while also discussing tag selection, noise handling, and scalability challenges.

AIRecommendation systemscollaborative filtering

0 likes · 35 min read

Social Tagging and Folksonomy in Recommendation Systems: Models, Algorithms, and Applications

DataFunTalk

Jun 4, 2020 · Artificial Intelligence

Exploring Federated Recommendation Algorithms and Their Applications

This article introduces the challenges of traditional centralized recommendation systems, explains the principles and implementations of federated recommendation algorithms—including vertical and horizontal federated matrix factorization and factorization machines—using WeBank’s open-source FATE platform, and discusses cloud services, practical use cases, and performance benefits.

AIFATEFederated Learning

0 likes · 13 min read

Exploring Federated Recommendation Algorithms and Their Applications

360 Tech Engineering

Aug 28, 2019 · Artificial Intelligence

Deep Collaborative Filtering Models and Their Implementation in Recommender Systems

This article surveys traditional and deep learning based collaborative filtering techniques—including similarity methods, matrix factorization, explicit and implicit feedback handling, various loss functions, evaluation metrics, and TensorFlow implementations of GMF, MLP, NeuMF, DMF, and ConvMF models—providing practical guidance for building large‑scale recommender systems.

TensorFlowcollaborative filteringdeep learning

0 likes · 21 min read

Deep Collaborative Filtering Models and Their Implementation in Recommender Systems

DataFunTalk

Jul 31, 2019 · Artificial Intelligence

Key Characteristics and Practical Improvements of Recommendation Technologies

This article discusses the fundamental traits of recommendation technologies, compares UserCF and ItemCF models, explains matrix factorization and FM, explores negative sampling, CTR/CVR modeling, ensemble methods, and practical considerations such as reinforcement learning and exploration strategies for improving recommendation performance in real-world systems.

CTR predictionFactorization MachinesMachine Learning

0 likes · 11 min read

Key Characteristics and Practical Improvements of Recommendation Technologies

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 learningdistributed systems

0 likes · 10 min read

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

Ctrip Technology

Jan 13, 2017 · Artificial Intelligence

Deep Learning Applications in Recommender Systems: A Hybrid Collaborative Filtering Model

This article reviews the early research on applying deep learning techniques such as autoencoders, stacked denoising autoencoders, and hybrid collaborative‑filtering models to recommender systems, describing the underlying matrix‑factorization theory, side‑information integration, experimental results, and future prospects.

Hybrid Modelautoencodercollaborative filtering

0 likes · 13 min read

Deep Learning Applications in Recommender Systems: A Hybrid Collaborative Filtering Model

Art of Distributed System Architecture Design

Aug 21, 2015 · Artificial Intelligence

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

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

ALSApache GiraphFacebook

0 likes · 9 min read