45 citations · 171 across the 18 of their papers we have counts for
5 papers · 1 filter
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…
Incorporating Recklessness to Collaborative Filtering based Recommender Systems
Diego Pérez-López, Fernando Ortega, Ángel González-Prieto +1
Recommender systems are intrinsically tied to a reliability/coverage dilemma: The more reliable we desire the forecasts, the more conservative the decision will be and thus, the fe…
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…
Deep Learning feature selection to unhide demographic recommender systems factors
Jesús Bobadilla, Ángel González-Prieto, Fernando Ortega +1
Extracting demographic features from hidden factors is an innovative concept that provides multiple and relevant applications. The matrix factorization model generates factors whic…