Publicaciones

Desarrollo de modelos de recomendación que incluyan técnicas de soft-computing y tratamiento de incertidumbre

1. 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.
2. 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 (pp. 21-38). Cham: Springer Nature Switzerland.
3. Pérez-López, D., Ortega, F., González-Prieto, Á., & Dueñas-Lerín, J. (2024). Incorporating recklessness to collaborative filtering based recommender systems. Information Sciences, 679, 121131.
4. 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
5. Bellogín, A. (2025). The Impact of Contexts in Contextual Matrix Factorization Models. In International Conference on Intelligent Data Engineering and Automated Learning (pp. 3-9). Cham: Springer Nature Switzerland.
6. Baldán, F. J., Yera, R., & Martínez, L. (2024). Natural noise management in collaborative recommender systems over time-related information. The Journal of Supercomputing, 80(16), 23628-23666.
7. Ranchal-Parrado, D., Porcel, C., Alcalá-Fdez, J. (2024). Propuesta de un esquema de recomendaciones híbrido basado en reglas secuenciales. En Actas de la XX Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2024). (pp. 625-626) (enlace)

Desarrollo de modelos de recomendación soportado por aprendizaje profundo

8. Bobadilla, J., & Gutiérrez, A. (2025). Use of conditional generative adversarial networks to create demographic collaborative filtering datasets. Applied Soft Computing, 169, 112608.
9. Bobadilla, J., & Gutiérrez, A. (2024). Wasserstein GAN-based architecture to generate collaborative filtering synthetic datasets. Applied Intelligence, 54(3), 2472-2490.
10. 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.
11. Bobadilla, J., & Gutiérrez, A. (2025). Testing Deep Learning Recommender Systems Models on Synthetic GAN-Generated Datasets. International Journal of Interactive Multimedia and Artificial Intelligence, 9(3), 30-39.
12. Bobadilla, J., & Gutierrez, A. (2025). Recommender Systems: Learning Collaborative Filtering Similarity Measures Using Siamese Networks. International Journal of Interactive Multimedia and Artificial Intelligence, 1-7.
13. Pérez-López, D., Bojorque, R., Dueñas-Lerín, J., & Ortega, F. (2025). Deep Learning-Based Stacking for Recommender Systems. In International Conference on Information and Communication Technology for Intelligent Systems (pp. 289-298). Singapore: Springer Nature Singapore.
14. Blanco-Mallo, E., Pérez-Núñez, P., Bolón-Canedo, V., & Remeseiro, B. (2025, November). Mitigating Overfitting in Recommender Systems via Intra-domain Transfer Learning. In International Conference on Intelligent Data Engineering and Automated Learning (pp. 71-83). Cham: Springer Nature Switzerland.
15. Pérez-Núñez, P., Buitelaar, P., Díez, J., Luaces, O., & Bahamonde, A. (2025). Attention-inspired text-based recommender system with explanatory capabilities. Applied Soft Computing, 113650.

Modelos de explicabilidad y equidad para sistemas de recomendación

16. Gómez, E., Contreras, D., Boratto, L., & Salamó, M. (2025). Enhancing recommender systems with provider fairness through preference distribution-awareness. International Journal of Information Management Data Insights, 5(1), 100311
17. Salcedo, M., & Bellogín, A. (2025). A Comparative Fairness Study in News Recommendation Systems. In International Conference on Intelligent Data Engineering and Automated Learning (pp. 10-22). Cham: Springer Nature Switzerland.
18. Darias, J. M., Bayrak, B., Caro-Martínez, M., Díaz-Agudo, B., & Recio-Garcia, J. A. (2024). An empirical analysis of user preferences regarding xai metrics. In International Conference on Case-Based Reasoning (pp. 96-110). Cham: Springer Nature Switzerland
19. González-Monge, M., Díaz-Agudo, B., & Recio-Garcia, J. A. (2025). Explaining Translational Embedding Models in Recommender Systems Using Knowledge Graphs and Language Models. In International Conference on Case-Based Reasoning (pp. 81-95). Springer, Cham.
20. Remadnia, O., Yera, R., Rodríguez, R. M., & Maazouzi, F. (2025). Post-hoc Recommendation Explanations in E-Learning: An Empirical Study Using Fuzzy Tools. In International Conference on Intelligent Data Engineering and Automated Learning (pp. 84-95). Cham: Springer Nature Switzerland.
21. Yera, R., Dutta, B., Martínez, F. J., & Martínez, L. (2025). Characterizing Provider Fairness in Content-Based E-Service Intelligent Recommendation. In International Conference on Intelligent and Fuzzy Systems (pp. 575-583). Cham: Springer Nature Switzerland.
22. Yera, R., & Martínez, L. (2024, November). Lore4grouprs: explaining group recommendations supported by a local rule-based approach. In International conference on intelligent data engineering and automated learning (pp. 300-312). Cham: Springer Nature Switzerland.
23. Sánchez, P. et al. (2024). Measuring and Mitigating Biases in Location-based Recommender Systems. En Actas de la XX Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2024). (pp. 615-616) (enlace)
24. Caro-Martinez, M. et al. (2024). A Formal Concept Analysis-based Method and A Graph Visualisation Approach to Enrich Explanations-by-examples. En Actas de la XX Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2024). (pp. 613-614) (enlace)
25. Alonso-Cortés, M. et al. (2024). Analyzing fairness of recommendations in e-participatory budgeting. En Actas de la XX Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2024). (pp. 619-620) (enlace)

Recomendación soportada por computación con palabras y toma de decisión multicriterio

26. Solano-Barliza, A., Valls, A., Acosta-Coll, M., Moreno, A., Escorcia-Gutierrez, J., De-La-Hoz-Franco, E., & Arregoces-Julio, I. (2024). Enhancing Fair Tourism Opportunities in Emerging Destinations by Means of Multi-criteria Recommender Systems: The Case of Restaurants in Riohacha, Colombia. International Journal of Computational Intelligence Systems, 17(1), 1-25
27. Salmony, M. Y., Moreno, A., & Valls, A. (2025). Advancing Sustainable Tourism Recommendations Through Innovative Hierarchical Evaluation and Data Acquisition Methods. In International Conference on Intelligent Data Engineering and Automated Learning (pp. 59-70). Cham: Springer Nature Switzerland.
28. Solano-Barliza, A., Valls, A., Moreno, A., Acosta-Coll, M., & Escorcia-Gutierrez, J. (2025). A logic multi-criteria recommender system for tourist services: Jimataa. International Journal of Information Technology, 1-25.
29. Solano-Barliza, A., Valls, A., Moreno, A., Herrera, J., De-La-Hoz-Franco, E., Acosta-Coll, M., & Escorcia-Gutierrez, J. (2025). Methodology for Designing a Tourism Recommender System: A Case Study of an Emerging Tourism Destination in Riohacha, Colombia. In International Conference on Computer Information Systems and Industrial Management (pp. 123-135). Cham: Springer Nature Switzerland.
30. Solano-Barliza, A., Valls, A., Moreno, A., Dujmovic, J., Acosta-Coll, M., Escorcia-Gutierrez, J., & De-La-Hoz-Franco, E. (2024). Personalized Hotel Recommender System Based on Graded Logic with Asymmetric Criteria. Procedia Computer Science, 246, 2864-2873.
31. Bani-Doumi, M., Serrano-Guerrero, J., Chiclana, F., Romero, F. P., & Olivas, J. A. (2024). A picture fuzzy set multi criteria decision-making approach to customize hospital recommendations based on patient feedback. Applied Soft Computing, 153, 111331

Nuevos modelos de recomendación grupal

32. Pérez-Almaguer, Y., Carballo-Cruz, E., Caballero-Mota, Y., & Yera, R. (2024). Exploring content-based group recommendation for suggesting restaurants in Havana City. Journal of Universal Computer Science, 30(1), 106-129.
33. Toledo, M., Yera, R., Barranco, M. J., & Dutta, B. (2025). GREX: A Platform for Supporting Explanations in Group Recommender Systems. In International Conference on Intelligent Data Engineering and Automated Learning (pp. 96-108). Cham: Springer Nature Switzerland.
34. Yera, R., Dutta, B., & Martínez, L. (2025). Using Shapley Additive Explanations for Supporting Individual and Group Recommendation: Survey and New Perspectives. In Decision-Making in Computational Intelligence-Based Systems: New Approaches, Methods, and Applications (pp. 3-30). Cham: Springer Nature Switzerland.

Nuevos escenarios y protocolos de experimentación para recomendación

35. Gómez, E., Contreras, D., Boratto, L., & Salamo, M. (2024). AMBAR: A dataset for Assessing Multiple Beyond-Accuracy Recommenders. In Proceedings of the 18th ACM Conference on Recommender Systems (pp. 137-147).
36. Alonso-Cortés, M., Cantador, I., & Bellogín, A. (2024). Recommendation Fairness in eParticipation: Listening to Minority, Vulnerable and NIMBY Citizens. In European Conference on Information Retrieval (pp. 420-436). Cham: Springer Nature Switzerland
37. Muñoz, P., Barba-Rojas, R., Díez, F., & Bellogín, A. (2025). The role of recommendation algorithms in the formation of disinformation networks. Information Processing & Management, 62(6), 104243.
38. Sánchez, P., Bellogin, A., & Jorro-Aragoneses, J. L. (2025). Context Trails: A Dataset to Study Contextual and Route Recommendation. In Proceedings of the Nineteenth ACM Conference on Recommender Systems (pp. 716-725).
39. Ungruh, R., Bellogín, A., Kowald, D., & Pera, M. S. (2025). Impacts of Mainstream-Driven Algorithms on Recommendations for Children Across Domains: A Reproducibility Study. In Proceedings of the Nineteenth ACM Conference on Recommender Systems (pp. 783-791).
40. Ungruh, R., Bellogín, A., & Pera, M. S. (2025). From Monolith to Mosaic: Uncovering Behavioral Differences for Choice Models in Recommender Systems Simulations. In Proceedings of the 48th international ACM SIGIR conference on research and development in information retrieval (pp. 2717-2722).
41. Waterschoot, C., Yera, R., Tintarev, N., & Barile, F. (2025). With Friends Like These, Who Needs Explanations? Evaluating User Understanding of Group Recommendations. In Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization (pp. 253-262).
42. Weissman, K., Salamanca, S., Sànchez-Marrè, M., Lechtenberg, F., Graells, M., & Espuña, A. (2024). Automatic recommendation of process technologies enabling circular economy. In International Congress on Environmental Modelling and Software, in iEMSs (p. 124).
43. Herce-Zelaya, J. et al. (2024). Propuesta de un conjunto de datos sintetico para estudiar el problema de arranque en frio en sistemas de recomendaciones. En Actas de la XX Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2024). (pp. 623-624) (enlace)

Modelos noveles de recomendación orientados a la salud

44. Barranco, M. J., Yera, R., & Martínez, F. J. (2025). A preference-based daily meal recommendation framework for patients with diabetes. Journal of Universal Computer Science (JUCS), 31(3).
45. Alshouha, B., Serrano-Guerrero, J., Chiclana, F., Romero, F. P., & Olivas, J. A. (2024). An integrated decision framework for evaluating and recommending health care services. Applied Intelligence, 54(5), 4153-4171.
46. 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.

Modelos noveles de recomendación orientados al turismo

47. Marzal, E., & Sebastia, L. (2024). Solving the tourist trip design problem with time windows and variable profit using incremental local search. Applied Soft Computing, 155, 111399.
48. Dietz, L. W., Sánchez, P., & Bellogín, A. (2025). Understanding the influence of data characteristics on the performance of point-of-interest recommendation algorithms. Information Technology & Tourism, 1-50.
49. Sánchez, P., & Bellogín, A. (2025). Smart imputation, better recommendations: improving traditional Point-of-Interest recommendation through data augmentation. ACM Transactions on Intelligent Systems and Technology, 16 (4), 1-35.
50. Arregocés-Julio, I., Solano-Barliza, A., Valls, A., Moreno, A., Castillo-Palacio, M., Acosta-Coll, M., & Escorcia-Gutierrez, J. (2025). A Flexible Profile-Based Recommender System for Discovering Cultural Activities in an Emerging Tourist Destination. In Informatics (Vol. 12, No. 3, p. 81).
51. Salmony, M. Y., Moreno, A., & Valls, A. (2025). Towards Sustainable Tourism: A Novel Comprehensive Hierarchical Multi-Criteria Evaluation Framework for Points-of-Interest Recommendations. In Artificial Intelligence Research and Development (pp. 12-21). IOS Press.
52. Almomani, A., Saavedra, P., Barreiro, P., Durán, R., Sánchez E., Crujeiras R., Loureiro M. (2024). Choice Models in Tourism Recommender Systems. En Actas de la XX Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2024). (pp. 627-628)
53. Pérez-Almaguer, Y., Carballo-Cruz, E., Caballero-Mota, Y., & Yera, R. (2024). Exploring content-based group recommendation for suggesting restaurants in Havana City. Journal of Universal Computer Science, 30(1), 106-129.
54. Solano-Barliza, A. et al. (2024). Diseño de sistemas recomendadores para destinos turísticos emergentes. En Actas de la XX Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2024). (pp. 609-610) (enlace)
55. Almomani, A. et al. (2024). Choice Models in Tourism Recommender Systems. En Actas de la XX Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2024). (pp. 627-628) (enlace)
56. Orama, J. et al. (2024). Social media profiling for personalized touristic recommendations. En Actas de la XX Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2024). (pp. 607-608) (enlace)

Surveys/Artículos de posición

57. Ariza-Casabona, A., Boratto, L., & Salamó, M. (2024). A Comparative Analysis of Text-Based Explainable Recommender Systems. In Proceedings of the 18th ACM Conference on Recommender Systems (pp. 105-115).
58. Mateos, P., & Bellogín, A. (2025). A systematic literature review of recent advances on context-aware recommender systems. Artificial Intelligence Review, 58(1), 1-53.
59. Yera, R., Carballo-Cruz, E., & Maroto-Martos, J. C. (2025). Group recommender systems for tourism: Current state and future directions. Intelligent Decision Technologies, 19 (4), 2212-2232.
60. Andrade-Ruiz, G., Carrasco, R. A., Porcel, C., Serrano-Guerrero, J., Mata, F., & Arias-Oliva, M. (2024). Emerging perspectives on the application of recommender systems in smart cities. Electronics, 13(7), 1249.
61. Jorro-Aragoneses, J. et al. (2024). On the formalization of the context-aware recommender systems design, building and evaluation processes. En Actas de la XX Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2024). (pp. 617-618) (enlace)