Fernando Ortega

Jorge Dueñas-Lerín

Inf. Sci., 2024

Comprehensive Evaluation of Matrix Factorization Models for Collaborative Filtering Recommender Systems.

[BibT_eX]

[DOI]

Int. J. Interact. Multim. Artif. Intell., 2024

2023

Using 3-D Printed Badges to Improve Student Performance and Reduce Dropout Rates in STEM Higher Education.

[BibT_eX]

[DOI]

Edgar Talavera

IEEE Trans. Educ., December, 2023

Neural group recommendation based on a probabilistic semantic aggregation.

[BibT_eX]

[DOI]

Neural Comput. Appl., July, 2023

Deep variational models for collaborative filtering-based recommender systems.

[BibT_eX]

[DOI]

Abraham Gutiérrez

Neural Comput. Appl., April, 2023

Deep Neural Aggregation for Recommending Items to Group of Users.

[BibT_eX]

[DOI]

CoRR, 2023

An evaluation framework for dimensionality reduction through sectional curvature.

[BibT_eX]

[DOI]

Diego Pérez-López

Diego Trujillo

CoRR, 2023

Using 3D printed badges to improve student performance and reduce dropout rates in STEM higher education.

[BibT_eX]

[DOI]

Edgar Talavera

CoRR, 2023

2022

Deep learning approach to obtain collaborative filtering neighborhoods.

[BibT_eX]

[DOI]

Neural Comput. Appl., 2022

ResBeMF: Improving Prediction Coverage of Classification based Collaborative Filtering.

[BibT_eX]

[DOI]

Abraham Gutiérrez

CoRR, 2022

Bias and Unfairness of Collaborative Filtering Based Recommender Systems in MovieLens Dataset.

[BibT_eX]

[DOI]

IEEE Access, 2022

2021

Deep learning feature selection to unhide demographic recommender systems factors.

[BibT_eX]

[DOI]

Neural Comput. Appl., 2021

CF4J 2.0: Adapting Collaborative Filtering for Java to new challenges of collaborative filtering based recommender systems.

[BibT_eX]

[DOI]

Jesús Mayor

Knowl. Based Syst., 2021

Providing reliability in recommender systems through Bernoulli Matrix Factorization.

[BibT_eX]

[DOI]

Inf. Sci., 2021

DeepFair: Deep Learning for Improving Fairness in Recommender Systems.

[BibT_eX]

[DOI]

Int. J. Interact. Multim. Artif. Intell., 2021

LEGO® Serious Play in Software Engineering Education.

[BibT_eX]

[DOI]

IEEE Access, 2021

2020

Collaborative Filtering to Predict Sensor Array Values in Large IoT Networks.

[BibT_eX]

[DOI]

Francisco Pajuelo-Holguera

Abraham Gutiérrez

Sensors, 2020

Recommender system implementations for embedded collaborative filtering applications.

[BibT_eX]

[DOI]

Juan Antonio Gómez Pulido

Francisco Pajuelo-Holguera

José M. Granado Criado

Microprocess. Microsystems, 2020

Classification-based Deep Neural Network Architecture for Collaborative Filtering Recommender Systems.

[BibT_eX]

[DOI]

Int. J. Interact. Multim. Artif. Intell., 2020

Recommender systems for sensor-based ambient control in academic facilities.

[BibT_eX]

[DOI]

Juan Antonio Gómez Pulido

Eng. Appl. Artif. Intell., 2020

Entornos parcialmente no euclidianos en realidad virtual(Partially non-Euclidean environments in virtual reality).

[BibT_eX]

[DOI]

Jesús Mayor

Francisco Pajuelo-Holguera

Proceedings of the VI Congreso de la Sociedad Española para las Ciencias del Videojuego, 2020

2019

MaliciousML: MovieLens dataset attacked with diverse profile injection attacks.

[BibT_eX]

[DOI]

Dataset, February, 2019

A Collaborative Filtering Approach Based on Naïve Bayes Classifier.

[BibT_eX]

[DOI]

IEEE Access, 2019

A New Recommendation Approach Based on Probabilistic Soft Clustering Methods: A Scientific Documentation Case Study.

[BibT_eX]

[DOI]

IEEE Access, 2019

Robust Model-Based Reliability Approach to Tackle Shilling Attacks in Collaborative Filtering Recommender Systems.

[BibT_eX]

[DOI]

IEEE Access, 2019

Evaluating Strategies for Selecting Test Datasets in Recommender Systems.

[BibT_eX]

[DOI]

Juan Antonio Gómez Pulido

Proceedings of the Hybrid Artificial Intelligent Systems - 14th International Conference, 2019

Motivation of Computer Science Engineering Students: Analysis and Recommendations.

[BibT_eX]

[DOI]

Edmundo Tovar

Pedro Pablo Alarcón

Proceedings of the IEEE Frontiers in Education Conference, 2019

2018

CF4J: Collaborative filtering for Java.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2018

<i>VisualRS</i>: Java framework for visualization of recommender systems information.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2018

Assigning reliability values to recommendations using matrix factorization.

[BibT_eX]

[DOI]

J. Comput. Sci., 2018

Reliability quality measures for recommender systems.

[BibT_eX]

[DOI]

Inf. Sci., 2018

Method to interactively visualize and navigate related information.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2018

An Efficient Recommender System Method Based on the Numerical Relevances and the Non-Numerical Structures of the Ratings.

[BibT_eX]

[DOI]

IEEE Access, 2018

Hybrid Collaborative Filtering Based on Users Rating Behavior.

[BibT_eX]

[DOI]

IEEE Access, 2018

Recommendation to Groups of Users Using the Singularities Concept.

[BibT_eX]

[DOI]

IEEE Access, 2018

Artificial Intelligence Scientific Documentation Dataset for Recommender Systems.

[BibT_eX]

[DOI]

IEEE Access, 2018

2017

A probabilistic model for recommending to new cold-start non-registered users.

[BibT_eX]

[DOI]

Inf. Sci., 2017

2016

A non negative matrix factorization for collaborative filtering recommender systems based on a Bayesian probabilistic model.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2016

Recommending items to group of users using Matrix Factorization based Collaborative Filtering.

[BibT_eX]

[DOI]

Inf. Sci., 2016

2015

Incorporating Group Recommendations to Recommender Systems: Alternatives and Performance.

[BibT_eX]

[DOI]

Fernando Ortega Requena

PhD thesis, 2015

2014

Hierarchical graph maps for visualization of collaborative recommender systems.

[BibT_eX]

[DOI]

J. Inf. Sci., 2014

Using Hierarchical Graph Maps to Explain Collaborative Filtering Recommendations.

[BibT_eX]

[DOI]

Int. J. Intell. Syst., 2014

2013

A similarity metric designed to speed up, using hardware, the recommender systems k-nearest neighbors algorithm.

[BibT_eX]

[DOI]

Guillermo Glez. de Rivera

Knowl. Based Syst., 2013

Recommender systems survey.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2013

Improving collaborative filtering-based recommender systems results using Pareto dominance.

[BibT_eX]

[DOI]

Inf. Sci., 2013

Incorporating reliability measurements into the predictions of a recommender system.

[BibT_eX]

[DOI]

Inf. Sci., 2013

Trees for explaining recommendations made through collaborative filtering.

[BibT_eX]

[DOI]

Inf. Sci., 2013

Incorporating group recommendations to recommender systems: Alternatives and performance.

[BibT_eX]

[DOI]

Inf. Process. Manag., 2013

2012

A collaborative filtering approach to mitigate the new user cold start problem.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2012

Collaborative filtering based on significances.

[BibT_eX]

[DOI]

Inf. Sci., 2012

A collaborative filtering similarity measure based on singularities.

[BibT_eX]

[DOI]

Inf. Process. Manag., 2012

A balanced memory-based collaborative filtering similarity measure.

[BibT_eX]

[DOI]

Int. J. Intell. Syst., 2012

Generalization of recommender systems: Collaborative filtering extended to groups of users and restricted to groups of items.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2012

2011

Improving collaborative filtering recommender system results and performance using genetic algorithms.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2011

A framework for collaborative filtering recommender systems.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2011

Extended Precision Quality Measure for Recommender Systems.

[BibT_eX]

[DOI]