Zobrazeno 1 - 10
of 1 085
pro vyhledávání: '"Ortega, Fernando"'
Publikováno v:
Information Sciences 442-443, 145-157 (2018)
Users want to know the reliability of the recommendations; they do not accept high predictions if there is no reliability evidence. Recommender systems should provide reliability values associated with the predictions. Research into reliability measu
Externí odkaz:
http://arxiv.org/abs/2402.04457
Publikováno v:
Knowledge-Based Systems, 152, 94-99 (2018)
Recommender Systems (RS) provide a relevant tool to mitigate the information overload problem. A large number of researchers have published hundreds of papers to improve different RS features. It is advisable to use RS frameworks that simplify RS res
Externí odkaz:
http://arxiv.org/abs/2402.01008
Autor:
Morales-Vargas, Abdías, Torres-Papaqui, Juan Pablo, Rosales-Ortega, Fernando Fabián, Chow-Martínez, Marcel, Ortega-Minakata, René Alberto, Robleto-Orús, Aitor Carlos, Collaboration, the CALIFA survey
We obtain the kinematic distributions of stars (synthetic model line absorption) and ionized gas (H$\alpha$ line emission) for star-forming regions residing in CALIFA survey tidally perturbed (perturbed) and non-tidally perturbed (control) galaxies.
Externí odkaz:
http://arxiv.org/abs/2309.09293
Recommender systems are intrinsically tied to a reliability/coverage dilemma: The more reliable we desire the forecasts, the more conservative the decision will be and thus, the fewer items will be recommended. This causes a detriment to the predicti
Externí odkaz:
http://arxiv.org/abs/2308.02058
Modern society devotes a significant amount of time to digital interaction. Many of our daily actions are carried out through digital means. This has led to the emergence of numerous Artificial Intelligence tools that assist us in various aspects of
Externí odkaz:
http://arxiv.org/abs/2307.09447
Autor:
Lara-Cabrera, Raúl, González-Prieto, Ángel, Pérez-López, Diego, Trujillo, Diego, Ortega, Fernando
Unsupervised machine learning lacks ground truth by definition. This poses a major difficulty when designing metrics to evaluate the performance of such algorithms. In sharp contrast with supervised learning, for which plenty of quality metrics have
Externí odkaz:
http://arxiv.org/abs/2303.09909
Students' perception of excessive difficulty in STEM degrees lowers their motivation and therefore affects their performance. According to prior research, the use of gamification techniques promote engagement, motivation and fun when learning. Badges
Externí odkaz:
http://arxiv.org/abs/2303.08939
Recommendation to groups of users is a challenging subfield of recommendation systems. Its key concept is how and where to make the aggregation of each set of user information into an individual entity, such as a ranked recommendation list, a virtual
Externí odkaz:
http://arxiv.org/abs/2303.07001