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 ]
• Dra. Sandra Arranz Paraiso (sandraar@ucm.es) [ ORCID ]
• Dra. Gema Bello (gema.borgaz@upm.es) [ ORCID ]
• Dr. Alberto Díaz-Álvarez (alberto.diaz@upm.es) [ ORCID ]
• Dr. Jorge Dueñas (jorge.duenas.lerin@upm.es)
• 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. Rodrigo Mariño (rodrigo.marino@upm.es) [ Google Scholar | ORCID ]
• Dr. Edgar Talavera (e.talavera@upm.es) [ ORCID ]

Students

• Guillermo Iglesias
• Diego Pérez-López

Recent publications (last 5 years)

2024

• González‐Miguéns, R., Cano, E., García‐Gallo Pinto, M., Peña, P. G., Rincón‐Barrado, M., Iglesias, G., … & Lara, E. (2024). The voice of the little giants: Arcellinida testate amoebae in environmental DNA‐based bioindication, from taxonomy free to haplotypic level. Molecular Ecology Resources, e13999.
• Iglesias, G., Talavera, E., Troya, J., Díaz-Álvarez, A., & García-Remesal, M. (2024). Artificial intelligence model for tumoral clinical decision support systems. Computer Methods and Programs in Biomedicine, 253, 108228. [ arXiv preprint | Source code ]
• Pérez-López, D., Ortega, F., González-Prieto, A., Dueñas-Lerín, J. (2024). Incorporating Recklessness to Collaborative Filtering based Recommender Systems, Information Sciences 679. [ arXiv preprint | Source code ]

2023

• Gutierrez-Cabello, G. S., Talavera, E., Iglesias, G., Clavijo, M., & Jiménez, F. (2023). A Novel Method to Generate Auto-Labeled Datasets for 3D Vehicle Identification Using a New Contrast Model. Applied Sciences, 13(7), 4334. [ Preprints.org preprint ]
• Iglesias, G., Talavera, E., & Díaz-Álvarez, A. (2023). A survey on GANs for computer vision: Recent research, analysis and taxonomy. Computer Science Review, 48, 100553. [ arXiv preprint ]
• Iglesias, G., Talavera, E., González-Prieto, Á., Mozo, A., & Gómez-Canaval, S. (2023). Data augmentation techniques in time series domain: a survey and taxonomy. Neural Computing and Applications, 35(14), 10123-10145. [ arXiv preprint ]
• 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 ]

2022

• 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.

2021

• 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.

2020

• 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, .

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. Jorge Dueñas Lerín. Recomendación a grupos de usuarios usando técnicas de aprendizaje profundo. Doctorado en Ciencias y Tecnologías de la Computación para Smart Cities, Universidad Politécnica de Madrid). Supervisors: D. Raúl Lara Cabrera & Dr. Fernando Ortega. April 2024.
• 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.