Hugo Alcaraz-Herrera

According to our database, Hugo Alcaraz-Herrera authored at least 8 papers between 2019 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

Using Coevolution and Substitution of the Fittest for Health and Well-Being Recommender Systems.

Hugo Alcaraz-Herrera, John Cartlidge

SN Comput. Sci., May, 2023

2022

EvoRecSys: Evolutionary framework for health and well-being recommender systems.

Hugo Alcaraz-Herrera, John Cartlidge, Zoi Toumpakari, Max Western, Iván Palomares

User Model. User Adapt. Interact., 2022

F-EvoRecSys: An Extended Framework for Personalized Well-Being Recommendations Guided by Fuzzy Inference and Evolutionary Computing.

Iván Palomares, Hugo Alcaraz-Herrera, Kao-Yi Shen

Int. J. Fuzzy Syst., 2022

Exploration of Ontological Representations for Evolutionary Computation.

Hugo Alcaraz-Herrera, John Cartlidge

Proceedings of the IEEE Congress on Evolutionary Computation, 2022

2021

Substitution of the Fittest: A Novel Approach for Mitigating Disengagement in Coevolutionary Genetic Algorithms.

Hugo Alcaraz-Herrera, John Cartlidge

Proceedings of the 13th International Joint Conference on Computational Intelligence, 2021

F-EvoRecSys: Fuzzy Inference meets Evolutionary Approach for Personalized Well-being Recommendations.

Iván Palomares, Hugo Alcaraz-Herrera, Kao-Yi Shen, Shabbir Syed-Abdul, Shwetambara Malwade

Proceedings of the 2021 International Conference on Fuzzy Theory and Its Applications, 2021

2019

Balancing Preferences, Popularity and Location in Context-Aware Restaurant Deal Recommendation: A Bristol Case Study.

Ercan Ezin, Hugo Alcaraz-Herrera, Iván Palomares

Proceedings of the Workshop on Recommenders in Tourism co-located with the 13th ACM Conference on Recommender Systems (RecSys 2019), 2019

Evolutionary approach for 'healthy bundle' wellbeing recommendations.

Hugo Alcaraz-Herrera, Iván Palomares

Proceedings of the 4th International Workshop on Health Recommender Systems co-located with the 13th ACM Conference on Recommender Systems 2019 (RecSys 2019), 2019