Resultados

Este apartado tiene como objetivo principal presentar las distintas publicaciones de los miembros de la Red ELIGE-IA

Publicaciones principales en revistas y congresos

Serrano-Guerrero, J., Bani-Doumi, M., Romero, F. P., & Olivas, J. A. (2024). A 2-tuple fuzzy linguistic model for recommending health care services grounded on aspect-based sentiment analysis. Expert Systems with Applications, 238, 122340. (Universidad de Castilla-La Mancha)

Gómez, E., Contreras, D., Boratto, L., & Salamó, M. (2024). MOReGIn: Multi-Objective Recommendation at the Global and Individual Levels. In European Conference on Information Retrieval, 2024. (Universidad de Barcelona)

L.M. de Campos, J.M. Fernández-Luna, J.F. Huete, An explainable
content-based approach for recommender systems: a case study in journal
recommendation for paper submission, User Modeling and User-Adapted
Interaction, 2024,  DOI https://doi.org/10.1007/s11257-024-09400-6. (Universidad de Granada)

Bobadilla, J., Gutiérrez, A., Yera, R., & Martínez, L. (2023). Creating Synthetic Datasets for Collaborative Filtering Recommender Systems using Generative Adversarial Networks. Knowledge-Based Systems, 280, 111016. (Universidad Politécnica de Madrid y Universidad de Jaén)

Pérez-Núñez, P., Díez, J., Remeseiro, B., Luaces, O., & Bahamonde, A. (2023). All-in-one picture: visual summary of items in a recommender system. Neural Computing and Applications, 35(27), 20339-20349. (Universidad de Oviedo)
 
 Pérez-Núñez, P., Díez, J., Luaces, O., Remeseiro, B., & Bahamonde, A. (2023). Users’ photos of items can reveal their tastes in a recommender system. Information Sciences, 642, 119227. (Universidad de Oviedo)
 
Botti-Cebriá, V., Sebastiá, L., Monzó, D., & Garcia, H. (2023). Improving the Scalability of Collaborative Filtering Recommendation with Clustering Techniques. In 2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) (pp. 64-71). IEEE. 
(Universidad Politécnica de Valencia)
 
Sánchez, P., Bellogín, A., & Boratto, L. (2023). Bias characterization, assessment, and mitigation in location-based recommender systems. Data Mining and Knowledge Discovery, 37, 1885-1929. (Universidad Autónoma de Madrid)
 
Amigó, E., Deldjoo, Y., Mizzaro, S., & Bellogín, A. (2023). A unifying and general account of fairness measurement in recommender systems. Information Processing & Management, 60(1), 103115.  (Universidad Autónoma de Madrid)
 
Ariza-Casabona, A., Salamó, M., Boratto, L., & Fenu, G. (2023). Towards Self-Explaining Sequence-Aware Recommendation. In Proceedings of the 17th ACM Conference on Recommender Systems (pp. 904-911). (Universidad de Barcelona)
 
Yera, R., Rodríguez, R. M., & Martínez, L. (2023). Exploring the Automatic Selection of Aggregation Methods in Group Recommendation. In Conference of the European Society for Fuzzy Logic and Technology (pp. 149-160). Cham: Springer Nature Switzerland. (Universidad de Jaén)
 
Herce-Zelaya, J., Porcel, C., Tejeda-Lorente, Á., Bernabé-Moreno, J., & Herrera-Viedma, E. (2022). Introducing CSP dataset: A dataset optimized for the study of the cold start problem in recommender systems. Information, 14(1), 19. (Universidad de Granada)
 
Dueñas-Lerín, J., Lara-Cabrera, R., Ortega, F., & Bobadilla, J. (2023). Neural group recommendation based on a probabilistic semantic aggregation. Neural Computing and Applications, 35(19), 14081-14092. (Universidad Politécnica de Madrid)
 
Bobadilla, J., Ortega, F., Gutiérrez, A., & González-Prieto, Á. (2023). Deep variational models for collaborative filtering-based recommender systems. Neural Computing and Applications, 35(10), 7817-7831. (Universidad Politécnica de Madrid)
 
Almomani, A., Saavedra, P., Barreiro, P., Durán, R., Crujeiras, R., Loureiro, M., & Sánchez, E. (2023). Application of choice models in tourism recommender systems. Expert Systems, 40(3), e13177. (Universidad de Santiago de Compostela)
 
Caro-Martínez, M., Jiménez-Díaz, G., & Recio-Garcia, J. A. (2023). A graph-based approach for minimising the knowledge requirement of explainable recommender systems. Knowledge and Information Systems, 65, 4379–4409. (Universidad Complutense de Madrid)
 
Caro-Martinez, M., Darias, J. M., Diaz-Agudo, B., & Recio-Garcia, J. A. (2023). iSeeE3—The Explanation Experiences Editor. SoftwareX, 21, 101311. (Universidad Complutense de Madrid)
 
Caro-Martínez, M., Jiménez-Díaz, G., & Recio-García, J. A. (2023). Local model-agnostic explanations for black-box recommender systems using interaction graphs and link prediction techniques. International Journal of Interactive Multimedia and Artificial Intelligence, 8(2), 202-212. (Universidad Complutense de Madrid)