Zobrazeno 1 - 10
of 393
pro vyhledávání: '"Cabrera,Raúl"'
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
Reliability measures associated with the prediction of the machine learning models are critical to strengthening user confidence in artificial intelligence. Therefore, those models that are able to provide not only predictions, but also reliability,
Externí odkaz:
http://arxiv.org/abs/2210.10619
Publikováno v:
Neural Computing and Applications, 1-18, 2020
Extracting demographic features from hidden factors is an innovative concept that provides multiple and relevant applications. The matrix factorization model generates factors which do not incorporate semantic knowledge. This paper provides a deep le
Externí odkaz:
http://arxiv.org/abs/2006.12379
Publikováno v:
International Journal of Interactive Multimedia and Artificial Intelligence, 2020
The lack of bias management in Recommender Systems leads to minority groups receiving unfair recommendations. Moreover, the trade-off between equity and precision makes it difficult to obtain recommendations that meet both criteria. Here we propose a
Externí odkaz:
http://arxiv.org/abs/2006.05255
Publikováno v:
Information Sciences, 2020
Beyond accuracy, quality measures are gaining importance in modern recommender systems, with reliability being one of the most important indicators in the context of collaborative filtering. This paper proposes Bernoulli Matrix Factorization (BeMF),
Externí odkaz:
http://arxiv.org/abs/2006.03481
Autor:
Játiva, Francisco, Tamayo, Juan Martin, Silva, Tommy, Granja, Oscar, Cabrera, Raul, Arce, Xavier, Guadalupe, Luis, Guillen, Mauricio, Koenders, Eddie, Lantsoght, Eva
Publikováno v:
In Procedia Structural Integrity 2024 64:1468-1475
Autor:
Balcazar-Ochoa, Luis Gerardo, Ventura-Martínez, Rosa, Ángeles-López, Guadalupe Esther, Gómez-Acevedo, Claudia, Carrasco, Omar Francisco, Sampieri-Cabrera, Raúl, Chavarría, Anahí, González-Hernández, Abimael
Publikováno v:
In Archives of Medical Research January 2024 55(1)