49 citations · 227 across the 12 of their papers we have counts for
12 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…
CF4J: Collaborative Filtering for Java
Fernando Ortega, Bo Zhu, Jesus Bobadilla +1
Recommender Systems (RS) provide a relevant tool to mitigate the information overload problem. A large number of researchers have published hundreds of papers to improve different…
Deep Neural Aggregation for Recommending Items to Group of Users
Jorge Dueñas-Lerín, Raúl Lara-Cabrera, Fernando Ortega +1
Modern society devotes a significant amount of time to digital interaction. Many of our daily actions are carried out through digital means. This has led to the emergence of numero…
Neural Group Recommendation Based on a Probabilistic Semantic Aggregation
Jorge Dueñas-Lerín, Raúl Lara-Cabrera, Fernando Ortega +1
Recommendation to groups of users is a challenging subfield of recommendation systems. Its key concept is how and where to make the aggregation of each set of user information into…
Creating Synthetic Datasets for Collaborative Filtering Recommender Systems using Generative Adversarial Networks
Jesús Bobadilla, Abraham Gutiérrez, Raciel Yera +1
Research and education in machine learning needs diverse, representative, and open datasets that contain sufficient samples to handle the necessary training, validation, and testin…
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…
DeepFair: Deep Learning for Improving Fairness in Recommender Systems
Jesús Bobadilla, Raúl Lara-Cabrera, Ángel González-Prieto +1
The lack of bias management in Recommender Systems leads to minority groups receiving unfair recommendations. Moreover, the trade-off between equity and precision makes it difficul…
Providing reliability in Recommender Systems through Bernoulli Matrix Factorization
Fernando Ortega, Raúl Lara-Cabrera, Ángel González-Prieto +1
Beyond accuracy, quality measures are gaining importance in modern recommender systems, with reliability being one of the most important indicators in the context of collaborative…