Matrix Factorization Techniques for Recommender Systems

2009/08/01 by Yehuda Koren, Robert Bell, Chris Volinsky · 459 citations
Computer Science · Business, Management and Accounting · #Recommender Systems and Techniques #Image Retrieval and Classification Techniques #Consumer Market Behavior and Pricing

paper · doi:10.1109/mc.2009.263

Abstract

As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.

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• Multivariate Information Fusion With Fast Kernel Learning to Kernel Ridge Regression in Predicting LncRNA-Protein Interactions. [europepmc]
• McImpute: Matrix Completion Based Imputation for Single Cell RNA-seq Data. [europepmc]
• A Novel Computational Model for Predicting microRNA-Disease Associations Based on Heterogeneous Graph Convolutional Networks. [europepmc]
• Drug-target interaction prediction using Multi Graph Regularized Nuclear Norm Minimization. [europepmc]
• Heterogeneous Multi-Layered Network Model for Omics Data Integration and Analysis. [europepmc]
• Prediction of circRNA-disease associations based on inductive matrix completion. [europepmc]
• Matrix factorization with neural network for predicting circRNA-RBP interactions. [europepmc]
• iDrug: Integration of drug repositioning and drug-target prediction via cross-network embedding. [europepmc]
• Evolution and impact of bias in human and machine learning algorithm interaction. [europepmc]
• News recommender system: a review of recent progress, challenges, and opportunities. [europepmc]
• A Benchmark for Data Imputation Methods. [europepmc]
• Interpretable deep recommender system model for prediction of kinase inhibitor efficacy across cancer cell lines. [europepmc]
• Artificial intelligence in E-Commerce: a bibliometric study and literature review. [europepmc]
• Machine learning-based ABA treatment recommendation and personalization for autism spectrum disorder: an exploratory study. [europepmc]
• Graph Representation Learning and Its Applications: A Survey. [europepmc]
• Advancing Computational Toxicology by Interpretable Machine Learning. [europepmc]
• Stacked ensembles on basis of parentage information can predict hybrid performance with an accuracy comparable to marker-based GBLUP. [europepmc]
• Computational drug repositioning with attention walking. [europepmc]
• Development of an Artificial Intelligence-Based Tailored Mobile Intervention for Nurse Burnout: Single-Arm Trial. [europepmc]

Related

• Feature-Based Matrix Factorization
• Clustering-Based Matrix Factorization
• Analysis and Visualization of the Parameter Space of Matrix Factorization-based Recommender Systems
• Dynamic recommender system : using cluster-based biases to improve the accuracy of the predictions
• An Introduction to Matrix factorization and Factorization Machines in Recommendation System, and Beyond