Fernando Ortega

According to our database, Fernando Ortega authored at least 55 papers between 2011 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

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

Raúl Lara-Cabrera, Fernando Ortega, Edgar Talavera, Daniel López-Fernández

IEEE Trans. Educ., December, 2023

Neural group recommendation based on a probabilistic semantic aggregation.

Jorge Dueñas-Lerín, Raúl Lara-Cabrera, Fernando Ortega, Jesús Bobadilla

Neural Comput. Appl., July, 2023

Deep variational models for collaborative filtering-based recommender systems.

Jesús Bobadilla, Fernando Ortega, Abraham Gutiérrez, Ángel González-Prieto

Neural Comput. Appl., April, 2023

Incorporating Recklessness to Collaborative Filtering based Recommender Systems.

Diego Pérez-López, Fernando Ortega, Ángel González-Prieto, Jorge Dueñas-Lerín

CoRR, 2023

Deep Neural Aggregation for Recommending Items to Group of Users.

Jorge Dueñas-Lerín, Raúl Lara-Cabrera, Fernando Ortega, Jesús Bobadilla

CoRR, 2023

An evaluation framework for dimensionality reduction through sectional curvature.

Raúl Lara-Cabrera, Ángel González-Prieto, Diego Pérez-López, Diego Trujillo, Fernando Ortega

CoRR, 2023

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

Raúl Lara-Cabrera, Fernando Ortega, Edgar Talavera, Daniel López-Fernández

CoRR, 2023

2022

Deep learning approach to obtain collaborative filtering neighborhoods.

Jesús Bobadilla, Ángel González-Prieto, Fernando Ortega, Raúl Lara-Cabrera

Neural Comput. Appl., 2022

ResBeMF: Improving Prediction Coverage of Classification based Collaborative Filtering.

Ángel González-Prieto, Abraham Gutiérrez, Fernando Ortega, Raúl Lara-Cabrera

CoRR, 2022

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

Álvaro González, Fernando Ortega, Diego Pérez-López, Santiago Alonso

IEEE Access, 2022

2021

Deep learning feature selection to unhide demographic recommender systems factors.

Jesús Bobadilla, Ángel González-Prieto, Fernando Ortega, Raúl Lara-Cabrera

Neural Comput. Appl., 2021

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

Fernando Ortega, Jesús Mayor, Daniel López-Fernández, Raúl Lara-Cabrera

Knowl. Based Syst., 2021

Providing reliability in recommender systems through Bernoulli Matrix Factorization.

Fernando Ortega, Raúl Lara-Cabrera, Ángel González-Prieto, Jesús Bobadilla

Inf. Sci., 2021

DeepFair: Deep Learning for Improving Fairness in Recommender Systems.

Jesús Bobadilla, Raúl Lara-Cabrera, Ángel González-Prieto, Fernando Ortega

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

LEGO® Serious Play in Software Engineering Education.

Daniel López-Fernández, Aldo Gordillo, Fernando Ortega, Agustín Yagüe, Edmundo Tovar

IEEE Access, 2021

2020

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

Fernando Ortega, Ángel González-Prieto, Jesús Bobadilla, Abraham Gutiérrez

Sensors, 2020

Recommender system implementations for embedded collaborative filtering applications.

Francisco Pajuelo-Holguera, Juan Antonio Gómez Pulido, Fernando Ortega, José M. Granado Criado

Microprocess. Microsystems, 2020

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

Jesús Bobadilla, Fernando Ortega, Abraham Gutiérrez, Santiago Alonso

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

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

Francisco Pajuelo-Holguera, Juan Antonio Gómez Pulido, Fernando Ortega

Eng. Appl. Artif. Intell., 2020

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

Jesús Mayor, Raúl Lara-Cabrera, Daniel López-Fernández, Fernando Ortega

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.

Santiago Alonso, Jesús Bobadilla, Fernando Ortega, Ricardo Moya

Dataset, February, 2019

A Collaborative Filtering Approach Based on Naïve Bayes Classifier.

Priscila Valdiviezo, Fernando Ortega, Eduardo Cobos, Raúl Lara-Cabrera

IEEE Access, 2019

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

Remigio Hurtado, Jesús Bobadilla, Rodolfo Bojorque, Fernando Ortega, Xin Li

IEEE Access, 2019

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

Santiago Alonso, Jesús Bobadilla, Fernando Ortega, Ricardo Moya

IEEE Access, 2019

Evaluating Strategies for Selecting Test Datasets in Recommender Systems.

Francisco Pajuelo-Holguera, Juan Antonio Gómez Pulido, Fernando Ortega

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

Motivation of Computer Science Engineering Students: Analysis and Recommendations.

Daniel López-Fernández, Edmundo Tovar, Pedro Pablo Alarcón, Fernando Ortega

Proceedings of the IEEE Frontiers in Education Conference, 2019

2018

CF4J: Collaborative filtering for Java.

Fernando Ortega, Bo Zhu, Jesús Bobadilla, Antonio Hernando

Knowl. Based Syst., 2018

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

S. Gil, Jesús Bobadilla, Fernando Ortega, Bo Zhu

Knowl. Based Syst., 2018

Assigning reliability values to recommendations using matrix factorization.

Bo Zhu, Fernando Ortega, Jesús Bobadilla, Abraham Gutiérrez

J. Comput. Sci., 2018

Reliability quality measures for recommender systems.

Jesús Bobadilla, Abraham Gutiérrez, Fernando Ortega, Bo Zhu

Inf. Sci., 2018

Method to interactively visualize and navigate related information.

Antonio Hernando, Jesús Bobadilla, Fernando Ortega, Abraham Gutiérrez

Expert Syst. Appl., 2018

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

Bo Zhu, Remigio Hurtado, Jesús Bobadilla, Fernando Ortega

IEEE Access, 2018

Hybrid Collaborative Filtering Based on Users Rating Behavior.

Fernando Ortega, Diego Rojo, Priscila Valdiviezo, Laura Raya

IEEE Access, 2018

Recommendation to Groups of Users Using the Singularities Concept.

Fernando Ortega, Remigio Hurtado, Jesús Bobadilla, Rodolfo Bojorque

IEEE Access, 2018

Artificial Intelligence Scientific Documentation Dataset for Recommender Systems.

Fernando Ortega, Jesús Bobadilla, Abraham Gutiérrez, Remigio Hurtado, Xin Li

IEEE Access, 2018

2017

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

Antonio Hernando, Jesús Bobadilla, Fernando Ortega, Abraham Gutiérrez

Inf. Sci., 2017

2016

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

Antonio Hernando, Jesús Bobadilla, Fernando Ortega

Knowl. Based Syst., 2016

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

Fernando Ortega, Antonio Hernando, Jesús Bobadilla, Jeon-Hyung Kang

Inf. Sci., 2016

2015

Incorporating Group Recommendations to Recommender Systems: Alternatives and Performance.

Fernando Ortega Requena

PhD thesis, 2015

2014

Hierarchical graph maps for visualization of collaborative recommender systems.

Antonio Hernando, Ricardo Moya, Fernando Ortega, Jesús Bobadilla

J. Inf. Sci., 2014

Using Hierarchical Graph Maps to Explain Collaborative Filtering Recommendations.

Fernando Ortega, Jesús Bobadilla, Antonio Hernando, Fernando Rodríguez

Int. J. Intell. Syst., 2014

2013

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

Jesús Bobadilla, Fernando Ortega, Antonio Hernando, Guillermo Glez. de Rivera

Knowl. Based Syst., 2013

Recommender systems survey.

Jesús Bobadilla, Fernando Ortega, Antonio Hernando, Abraham Gutiérrez

Knowl. Based Syst., 2013

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

Fernando Ortega, José Luis Sánchez, Jesús Bobadilla, Abraham Gutiérrez

Inf. Sci., 2013

Incorporating reliability measurements into the predictions of a recommender system.

Antonio Hernando, Jesús Bobadilla, Fernando Ortega, Jorge Tejedor

Inf. Sci., 2013

Trees for explaining recommendations made through collaborative filtering.

Antonio Hernando, Jesús Bobadilla, Fernando Ortega, Abraham Gutiérrez

Inf. Sci., 2013

Incorporating group recommendations to recommender systems: Alternatives and performance.

Fernando Ortega, Jesús Bobadilla, Antonio Hernando, Abraham Gutiérrez

Inf. Process. Manag., 2013

2012

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

Jesús Bobadilla, Fernando Ortega, Antonio Hernando, Jesus Bernal

Knowl. Based Syst., 2012

Collaborative filtering based on significances.

Jesús Bobadilla, Antonio Hernando, Fernando Ortega, Abraham Gutiérrez

Inf. Sci., 2012

A collaborative filtering similarity measure based on singularities.

Jesús Bobadilla, Fernando Ortega, Antonio Hernando

Inf. Process. Manag., 2012

A balanced memory-based collaborative filtering similarity measure.

Jesús Bobadilla, Fernando Ortega, Antonio Hernando, Angel Arroyo

Int. J. Intell. Syst., 2012

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

Jesús Bobadilla, Fernando Ortega, Antonio Hernando, Jesus Bernal

Expert Syst. Appl., 2012

2011

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

Jesús Bobadilla, Fernando Ortega, Antonio Hernando, Javier Alcalá

Knowl. Based Syst., 2011

A framework for collaborative filtering recommender systems.

Jesús Bobadilla, Antonio Hernando, Fernando Ortega, Jesus Bernal

Expert Syst. Appl., 2011

Extended Precision Quality Measure for Recommender Systems.

Fernando Ortega, Antonio Hernando, Jesús Bobadilla

Proceedings of the Advances in Artificial Intelligence, 2011