47 citations · 86 across the 6 of their papers we have counts for
6 papers
Comprehensive Evaluation of Matrix Factorization Models for Collaborative Filtering Recommender Systems
Jesús Bobadilla, Jorge Dueñas-Lerín, Fernando Ortega +1
Matrix factorization models are the core of current commercial collaborative filtering Recommender Systems. This paper tested six representative matrix factorization models, using…
Testing Deep Learning Recommender Systems Models on Synthetic GAN-Generated Datasets
Jesús Bobadilla, Abraham Gutiérrez
The published method Generative Adversarial Networks for Recommender Systems (GANRS) allows generating data sets for collaborative filtering recommendation systems. The GANRS sourc…
Neural Collaborative Filtering Classification Model to Obtain Prediction Reliabilities
Jesús Bobadilla, Abraham Gutiérrez, Santiago Alonso +1
Neural collaborative filtering is the state of art field in the recommender systems area; it provides some models that obtain accurate predictions and recommendations. These models…
Reliability quality measures for recommender systems
Jesús Bobadilla, Abraham Gutierrez, Fernando Ortega +1
Users want to know the reliability of the recommendations; they do not accept high predictions if there is no reliability evidence. Recommender systems should provide reliability v…
Restricted Bernoulli Matrix Factorization: Balancing the trade-off between prediction accuracy and coverage in classification based collaborative filtering
Ángel González-Prieto, Abraham Gutiérrez, Fernando Ortega +1
Reliability measures associated with the prediction of the machine learning models are critical to strengthening user confidence in artificial intelligence. Therefore, those models…
Deep Variational Models for Collaborative Filtering-based Recommender Systems
Jesús Bobadilla, Fernando Ortega, Abraham Gutiérrez +1
Deep learning provides accurate collaborative filtering models to improve recommender system results. Deep matrix factorization and their related collaborative neural networks are…