no code implementations • 6 Feb 2024 • Jesús Bobadilla, Abraham Gutierrez, Fernando Ortega, Bo Zhu
Both quality measures are based on the hypothesis that the more suitable a reliability measure is, the better accuracy results it will provide when applied.
1 code implementation • 1 Feb 2024 • Fernando Ortega, Bo Zhu, Jesus Bobadilla, Antonio Hernando
Recommender Systems (RS) provide a relevant tool to mitigate the information overload problem.
1 code implementation • 3 Aug 2023 • Diego Pérez-López, Fernando Ortega, Ángel González-Prieto, Jorge Dueñas-Lerín
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.
1 code implementation • 18 Jul 2023 • Jorge Dueñas-Lerín, Raúl Lara-Cabrera, Fernando Ortega, Jesús Bobadilla
One key tool for the digital society is Recommender Systems, intelligent systems that learn from our past actions to propose new ones that align with our interests.
1 code implementation • 17 Mar 2023 • Raúl Lara-Cabrera, Ángel González-Prieto, Diego Pérez-López, Diego Trujillo, Fernando Ortega
This poses a major difficulty when designing metrics to evaluate the performance of such algorithms.
1 code implementation • 13 Mar 2023 • Jorge Dueñas-Lerín, Raúl Lara-Cabrera, Fernando Ortega, Jesús Bobadilla
Recommendation to groups of users is a challenging subfield of recommendation systems.
1 code implementation • 5 Oct 2022 • Ángel González-Prieto, Abraham Gutiérrez, Fernando Ortega, Raúl Lara-Cabrera
Reliability measures associated with the prediction of the machine learning models are critical to strengthening user confidence in artificial intelligence.
1 code implementation • 27 Jul 2021 • Jesús Bobadilla, Fernando Ortega, Abraham Gutiérrez, Ángel González-Prieto
On the other hand, data augmentation through variational autoencoder does not provide accurate results in the collaborative filtering field due to the high sparsity of recommender systems.
no code implementations • 17 Jun 2020 • Jesús Bobadilla, Ángel González-Prieto, Fernando Ortega, Raúl Lara-Cabrera
This paper provides a deep learning-based method: DeepUnHide, able to extract demographic information from the users and items factors in collaborative filtering recommender systems.
no code implementations • 9 Jun 2020 • Jesús Bobadilla, Raúl Lara-Cabrera, Ángel González-Prieto, Fernando Ortega
The lack of bias management in Recommender Systems leads to minority groups receiving unfair recommendations.
1 code implementation • 5 Jun 2020 • Fernando Ortega, Raúl Lara-Cabrera, Ángel González-Prieto, Jesús Bobadilla
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.