scholarly journals Recommender Systems Clustering Using Bayesian Non Negative Matrix Factorization

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 3549-3564 ◽  
Author(s):  
Jesus Bobadilla ◽  
Rodolfo Bojorque ◽  
Antonio Hernando Esteban ◽  
Remigio Hurtado
Keyword(s):  
Recommender Systems ◽  
Matrix Factorization ◽  
Non Negative Matrix Factorization

scholarly journals A Survey of Latent Factor Models for Recommender Systems and Personalization

2021 ◽  
Author(s):  
Shalin Shah
Keyword(s):  
Recommender Systems ◽  
Matrix Factorization ◽  
Matrix Completion ◽  
Factor Models ◽  
Singular Value ◽  
Bayesian Personalized Ranking ◽  
Latent Factor Models ◽  
Value Decomposition ◽  
Personalized Ranking ◽  
Non Negative Matrix Factorization

<p>Recommender systems aim to personalize the experience of a user and are critical for businesses like retail portals, e-commerce websites, book sellers, streaming movie websites and so on. The earliest personalized algorithms use matrix factorization or matrix completion using algorithms like the singular value decomposition (SVD). There are other more advanced algorithms, like factorization machines, Bayesian personalized ranking (BPR), and a more recent Hebbian graph embeddings (HGE) algorithm. In this work, we implement BPR and HGE and compare our results with SVD, Non-negative matrix factorization (NMF) using the MovieLens dataset.</p>

scholarly journals A Survey of Latent Factor Models for Recommender Systems and Personalization

2021 ◽  
Author(s):  
Shalin Shah
Keyword(s):  
Recommender Systems ◽  
Matrix Factorization ◽  
Matrix Completion ◽  
Factor Models ◽  
Singular Value ◽  
Bayesian Personalized Ranking ◽  
Latent Factor Models ◽  
Value Decomposition ◽  
Personalized Ranking ◽  
Non Negative Matrix Factorization

<p>Recommender systems aim to personalize the experience of a user and are critical for businesses like retail portals, e-commerce websites, book sellers, streaming movie websites and so on. The earliest personalized algorithms use matrix factorization or matrix completion using algorithms like the singular value decomposition (SVD). There are other more advanced algorithms, like factorization machines, Bayesian personalized ranking (BPR), and a more recent Hebbian graph embeddings (HGE) algorithm. In this work, we implement BPR and HGE and compare our results with SVD, Non-negative matrix factorization (NMF) using the MovieLens dataset.</p>

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