Fernando Ortega

  • Dpto. Sistemas Informáticos, ETSI Sistemas Informáticos, Universidad Politécnica" de Madrid, Spain (since 2018)
  • U-tad: Centro universitario de tecnología y arte digital, Madrid, Spain (since 2017)
  • BigTrueData, Madrid, Spain (2015 - 2017)
  • Technical University of Madrid, Spain

According to our database1, Fernando Ortega authored at least 46 papers between 2011 and 2022.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.



In proceedings 
PhD thesis 


Online presence:

On csauthors.net:


Deep learning approach to obtain collaborative filtering neighborhoods.
Neural Comput. Appl., 2022

Bias and Unfairness of Collaborative Filtering Based Recommender Systems in MovieLens Dataset.
IEEE Access, 2022

Deep learning feature selection to unhide demographic recommender systems factors.
Neural Comput. Appl., 2021

CF4J 2.0: Adapting Collaborative Filtering for Java to new challenges of collaborative filtering based recommender systems.
Knowl. Based Syst., 2021

Providing reliability in recommender systems through Bernoulli Matrix Factorization.
Inf. Sci., 2021

DeepFair: Deep Learning for Improving Fairness in Recommender Systems.
Int. J. Interact. Multim. Artif. Intell., 2021

Deep Variational Models for Collaborative Filtering-based Recommender Systems.
CoRR, 2021

LEGO® Serious Play in Software Engineering Education.
IEEE Access, 2021

Collaborative Filtering to Predict Sensor Array Values in Large IoT Networks.
Sensors, 2020

Recommender system implementations for embedded collaborative filtering applications.
Microprocess. Microsystems, 2020

Classification-based Deep Neural Network Architecture for Collaborative Filtering Recommender Systems.
Int. J. Interact. Multim. Artif. Intell., 2020

Recommender systems for sensor-based ambient control in academic facilities.
Eng. Appl. Artif. Intell., 2020

Entornos parcialmente no euclidianos en realidad virtual(Partially non-Euclidean environments in virtual reality).
Proceedings of the VI Congreso de la Sociedad Española para las Ciencias del Videojuego, 2020

A Collaborative Filtering Approach Based on Naïve Bayes Classifier.
IEEE Access, 2019

A New Recommendation Approach Based on Probabilistic Soft Clustering Methods: A Scientific Documentation Case Study.
IEEE Access, 2019

Robust Model-Based Reliability Approach to Tackle Shilling Attacks in Collaborative Filtering Recommender Systems.
IEEE Access, 2019

Evaluating Strategies for Selecting Test Datasets in Recommender Systems.
Proceedings of the Hybrid Artificial Intelligent Systems - 14th International Conference, 2019

Motivation of Computer Science Engineering Students: Analysis and Recommendations.
Proceedings of the IEEE Frontiers in Education Conference, 2019

CF4J: Collaborative filtering for Java.
Knowl. Based Syst., 2018

<i>VisualRS</i>: Java framework for visualization of recommender systems information.
Knowl. Based Syst., 2018

Assigning reliability values to recommendations using matrix factorization.
J. Comput. Sci., 2018

Reliability quality measures for recommender systems.
Inf. Sci., 2018

Method to interactively visualize and navigate related information.
Expert Syst. Appl., 2018

An Efficient Recommender System Method Based on the Numerical Relevances and the Non-Numerical Structures of the Ratings.
IEEE Access, 2018

Hybrid Collaborative Filtering Based on Users Rating Behavior.
IEEE Access, 2018

Recommendation to Groups of Users Using the Singularities Concept.
IEEE Access, 2018

Artificial Intelligence Scientific Documentation Dataset for Recommender Systems.
IEEE Access, 2018

A probabilistic model for recommending to new cold-start non-registered users.
Inf. Sci., 2017

A non negative matrix factorization for collaborative filtering recommender systems based on a Bayesian probabilistic model.
Knowl. Based Syst., 2016

Recommending items to group of users using Matrix Factorization based Collaborative Filtering.
Inf. Sci., 2016

Hierarchical graph maps for visualization of collaborative recommender systems.
J. Inf. Sci., 2014

Using Hierarchical Graph Maps to Explain Collaborative Filtering Recommendations.
Int. J. Intell. Syst., 2014

A similarity metric designed to speed up, using hardware, the recommender systems k-nearest neighbors algorithm.
Knowl. Based Syst., 2013

Recommender systems survey.
Knowl. Based Syst., 2013

Improving collaborative filtering-based recommender systems results using Pareto dominance.
Inf. Sci., 2013

Incorporating reliability measurements into the predictions of a recommender system.
Inf. Sci., 2013

Trees for explaining recommendations made through collaborative filtering.
Inf. Sci., 2013

Incorporating group recommendations to recommender systems: Alternatives and performance.
Inf. Process. Manag., 2013

A collaborative filtering approach to mitigate the new user cold start problem.
Knowl. Based Syst., 2012

Collaborative filtering based on significances.
Inf. Sci., 2012

A collaborative filtering similarity measure based on singularities.
Inf. Process. Manag., 2012

A balanced memory-based collaborative filtering similarity measure.
Int. J. Intell. Syst., 2012

Generalization of recommender systems: Collaborative filtering extended to groups of users and restricted to groups of items.
Expert Syst. Appl., 2012

Improving collaborative filtering recommender system results and performance using genetic algorithms.
Knowl. Based Syst., 2011

A framework for collaborative filtering recommender systems.
Expert Syst. Appl., 2011

Extended Precision Quality Measure for Recommender Systems.
Proceedings of the Advances in Artificial Intelligence, 2011