KNODIS research group

About KNODIS

KNOwledge Discovery & Information Systems (KNODIS) is a research group located at the Universidad Politécnica de Madrid. Our area of research focuses on machine learning and data science. More specifically, our research focuses on the sub-areas of recommender systems, deep learning and combinatorial optimization. In addition, given the teaching nature of the university, we are involved in various educational innovation projects. We have a proven research background with dozens of publications in leading international scientific journals and we work actively with industry to transfer the knowledge resulting from our research to society.

Members

Research group leader

• Dr. Fernando Ortega (fernando.ortega@upm.es) [ Google Scholar | ORCID ]

Research team

• Dr. Santiago Alonso (santiago.alonso@upm.es) [ ORCID ]
• Dr. Felix Fuentes (felix.fuentes@upm.es)
• Dr. Ángel González-Prieto (angelgonzalezprieto@ucm.es) [ ORCID ]
• Dr. Raúl Lara-Cabrera (raul.lara@upm.es) [ ORCID ]
• Dr. Edgar Talavera (e.talavera@upm.es) [ ORCID ]
• Dr. Alberto Díaz-Álvarez (alberto.diaz@upm.es) [ ORCID ]

Students

• Jorge Dueñas
• Guillermo Iglesias
• Diego Pérez-López

Recent publications (last 5 years)

• Dueñas-Lerín, J., Lara-Cabrera, R., Ortega, F., & Bobadilla, J. (2023). Neural group recommendation based on a probabilistic semantic aggregation, Neural Computing and Applications, In press. [ arXiv preprint | Source code ]
• Bobadilla, J., Ortega, F., González-Prieto, A., & Gutierrez, A. (2022). Deep Variational Models for Collaborative Filtering-based Recommender Systems, Neural Computing and Applications, 1-15. [ Open Access | Source code ]
• A. González, F. Ortega, D. Pérez-López, & S. Alonso (2022). Bias and unfairness of collaborative filtering based recommender systems in MovieLens dataset, IEEE Access, 10, 68429-68439. [ Open Access ]
• Bobadilla, J., Dueñas, J., Gutiérrez, A., & Ortega, F. (2022). Deep Variational Embedding Representation on Neural Collaborative Filtering Recommender Systems, Applied Sciences-Basel 12 (9), 4168. [ Open Access | arXiv preprint
• Lara-Cabrera, R., González, A., Ortega, F., & González-Prieto, A. (2022). Dirichlet Matrix Factorization: A Reliable Classification-Based Recommender System, Applied Sciences-Basel 12 (3), 1223. [ Open Access | Source code ]
• Bobadilla, J., González-Prieto, Á., Ortega, F., & Lara-Cabrera, R. (2022). Deep Learning Approach to Obtain Collaborative Filtering Neighborhoods, Neural Computing and Applications 34, In Press.
• López-Fernández, D., Gordillo, A., Ortega, F., Yague, A., & Tovar, E. (2021). LEGO® Serious Play in Software Engineering Education, IEEE Access 9, 103120-103131.
• Ortega, F., Lara-Cabrera, R., González-Prieto, A., & Bobadilla, J. (2021). Providing Reliability in Recommender Systems through Bernoulli Matrix Factorization, Information Sciences 553, 110-128. [ arXiv preprint | Source code ]
• Ortega, F., Mayor, J., López-Fernández, D., & Lara-Cabrera, R. (2021). CF4J 2.0: Adapting Collaborative Filtering for Java to New Challenges of Collaborative Filtering based Recommender Systems, Knowledge-Based Systems 215, 106629. [ Source code ]
• Bobadilla, J., Lara-Cabrera, R., González-Prieto, Á., & Ortega, F. (2021). DeepFair: Deep Learning for Improving Fairness in Recommender Systems, International Journal of Interactive Multimedia and Artificial Intelligence 6, 86-94. [ Open Access | arXiv preprint ]
• Bobadilla, J., González-Prieto, Á., Ortega, F., & Lara-Cabrera, R. (2021). Deep Learning feature selection to unhide demographic recommender systems factors, Neural Computing and Applications 33 (12), 7291-7308. [ arXiv preprint ]
• Pajuelo-Holgera, F., Gómez-Pulido, J.A., & Ortega, F. (2020). Recommender Systems for Sensor-based Ambient Control in Academic Facilities, Engineering Applications of Artificial Intelligence Article 96, 103993.
• Ortega, F., Gonzalez-Prieto, A., Bobadilla. J., & Gutierrez, A. (2020). Collaborative Filtering to Predict Sensor Array Values in Large IoT Networks, Sensors 20 (16), 4628. [ Open Access ]
• Lara-Cabrera, R., González-Prieto, A., & Ortega, F. (2020). Deep Matrix Factorization approach for Collaborative Filtering Recommender Systems, Applied Sciences-Basel 10 (14), 4926. [ Open Access | Source code ]
• Pajuelo-Holgera, F., Gómez-Pulido, J.A., & Ortega, F. (2020). Performance of Two Approaches of Embedded Recommender Systems, Electronics 9 (4), 546.
• Bobadilla, J., Ortega, F., Gutierrez, A. & Alonso, S. (2020). Classification-based Deep Neural Network Architecture for Collaborative Filtering Recommender Systems, International Journal of Interactive Multimedia and Artificial Intelligence 6 (1), 68-77. [ Open Access ]
• Lara-Cabrera, R., González-Prieto, A., Ortega, F., & Bobadilla, J. (2020). Evolving Matrix Factorization based Collaborative Filtering using Genetic Programming, Applied Sciences-Basel 10 (2), 675. [ Open Access | Source code ]
• Pajuelo-Holgera, F., Gómez-Pulido, J.A., Ortega, F., & Granado-Criado J.M. (2020). Recommender system implementations for embedded collaborative filtering applications, Microprocessors and Microsystems 73, .
• Valdiviezo, P., Ortega, F., Cobos, E., & Lara-Cabrera, R. (2019). A Collaborative Filtering approach based on Naïve Bayes Classifier, IEEE Access 7, 108581-108592. [ Open Access ]
• López-Fernández, D., Raya, L., Ortega, F., & García, J.J. (2019). Project Based Learning Meets Service Learning on Software Development Education, International Journal of Engineering Education 35 (5), 1436-1445.
• Alonso, S., Bobadilla, J., Ortega, F., & Moya, R. (2019). Robust model-based reliability approach to tackle shilling attacks in collaborative filtering recommender systems, IEEE Access 7, 41782-41798. [ Open Access ]

Active research projects

• DL-CEGM: Aumento de la calidad y de la equidad, a grupos minoritarios, en las recomendaciones obtenidas mediante filtrado colaborativo basado en técnicas de Deep Learning (PID2019-106493RB-I00). Project funded by Ministerio de Ciencia, Innovación y Universidades. From jun 2020 to may 2023. Principal Investigator: Jesús Bobadilla.

Thesis

• D. Francisco Pajuelo Holguera. Sistemas de recomendación basados en filtrado colaborativo: aceleración mediante computación reconfigurable y aplicaciones predictivas sensoriales. Doctorado en Tecnología Aeroespacial: Ingenierías Electromagnética, Electrónica, Informática y Mecánica (Universidad de Extremadura). Supervisors: Dr. Juan Antonio Gómez-Pulido & Dr. Fernando Ortega. July 2021.
• D. Remigio Hurtado Ortiz. Recomendación a grupos de usuarios usando el concepto de singularidades. Doctorado en Ciencias y Tecnologías de la Computación para Smart Cities (Universidad Politécnica de Madrid). Supervisors: Dr. Jesús Bobadilla & Dr. Fernando Ortega. February 2020.
• D. Rodolfo Bojorque Chasi. Clustering de sistemas de recomendación mediante técnicas de factorización matricial. Doctorado en Ciencias y Tecnologías de la Computación para Smart Cities (Universidad Politécnica de Madrid). Supervisors: Dr. Jesús Bobadilla & Dr. Fernando Ortega. February 2020.
• Dª. Priscila Marisela Valdiviezo Diaz. Sistema recomendador híbrido basado en modelos probabilísticos. Doctorado en Ciencias y Tecnologías de la Computación para Smart Cities (Universidad Politécnica de Madrid). Supervisors: Dr. Jesús Bobadilla & Dr. Antonio Hernando. September 2019.

Research resources

• Collaborative Filtering for Java (CF4J): an object-oriented Java framework designed for the trial and error process for Collaborative Filtering research.
• make_spirals: a python module to generates a synthetic data set composed of interlaced Archimedean spirals.

You can also visit our organization in GitHub.