PUBLICACIÓN

Collaborative filtering optimization based on matrix factorization through the relevance of user preferences

ACCEDER A LA PUBLICACIÓN: Scopus

Valdiviezo-Diaz P.; Ortega F.; Mayor J.; Pajuelo-Holguera F.

2020 RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao

Computer Science (miscellaneous) (Q4)

SJR: 0.142

CITAS

1

EID

2-s2.0-85081022785

ISSN

1646-9895

AUTORES DE LA UEX

Francisco Pajuelo Holguera

OTRAS PUBLICACIONES DE INTERÉS

Selecting Latent Factors When Predicting Student Performance In Online Campus By Using Recommender Systems
Fast Montgomery Modular Multiplier Using Fpgas
Collaborative Filtering Optimization Based On Matrix Factorization Through The Relevance Of User Preferences
Fast Montgomery Modular Multiplier Using Fpgas
Collaborative Filtering Optimization Based On Matrix Factorization Through The Relevance Of User Preferences
Recommender Systems For Sensor-Based Ambient Control In Academic Facilities
Collaborative Filtering Optimization Based On Matrix Factorization Through The Relevance Of User Preferences
Recommender Systems For Sensor-Based Ambient Control In Academic Facilities
Optimizing Latent Factors And Collaborative Filtering For Students' Performance Prediction
Recommender Systems For Sensor-Based Ambient Control In Academic Facilities
Optimizing Latent Factors And Collaborative Filtering For Students' Performance Prediction
Performance Of Two Approaches Of Embedded Recommender Systems
Optimizing Latent Factors And Collaborative Filtering For Students' Performance Prediction
Performance Of Two Approaches Of Embedded Recommender Systems
Recommender System Implementations For Embedded Collaborative Filtering Applications
Performance Of Two Approaches Of Embedded Recommender Systems
Recommender System Implementations For Embedded Collaborative Filtering Applications
Evaluating Strategies For Selecting Test Datasets In Recommender Systems
Recommender System Implementations For Embedded Collaborative Filtering Applications
Evaluating Strategies For Selecting Test Datasets In Recommender Systems
Evaluating Strategies For Selecting Test Datasets In Recommender Systems
Evaluating Strategies For Selecting Test Datasets In Recommender Systems
Evaluating Strategies For Selecting Test Datasets In Recommender Systems
Selecting Latent Factors When Predicting Student Performance In Online Campus By Using Recommender Systems
Evaluating Strategies For Selecting Test Datasets In Recommender Systems
Selecting Latent Factors When Predicting Student Performance In Online Campus By Using Recommender Systems
Fast Montgomery Modular Multiplier Using Fpgas
WEB ACCESIBLE