no code implementations • 19 Jan 2024 • Jorge Paz-Ruza, Amparo Alonso-Betanzos, Bertha Guijarro-Berdiñas, Brais Cancela, Carlos Eiras-Franco
Dyadic regression models, which predict real-valued outcomes for pairs of entities, are fundamental in many domains (e. g. predicting the rating of a user to a product in Recommender Systems) and promising and under exploration in many others (e. g. approximating the adequate dosage of a drug for a patient in personalized pharmacology).
no code implementations • 28 Jul 2023 • Alejandro Rodríguez-Arias, Amparo Alonso-Betanzos, Bertha Guijarro-Berdiñas, Noelia Sánchez-Marroño
In this paper, we propose an ABM architecture that allows us to analyze the evolution of virus infections in a society based on two components: 1) an adaptation of the SEIRD model and 2) a decision-making model for citizens.
1 code implementation • 27 Jul 2023 • Jorge Paz-Ruza, Amparo Alonso-Betanzos, Berta Guijarro-Berdiñas, Brais Cancela, Carlos Eiras-Franco
Recommender Systems have become crucial in the modern world, commonly guiding users towards relevant content or products, and having a large influence over the decisions of users and citizens.
no code implementations • Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 26th International Conference KES2022 2022 • Jorge Paz-Ruza, Carlos Eiras-Franco, Bertha Guijarro-Berdiñas, Amparo Alonso-Betanzos
Systems that rely on dyadic data, which relate entities of two types together, have become ubiquitously used in fields such as media services, tourism business, e-commerce, and others.
Explainable artificial intelligence Image-based Recommendation Explainability +1
no code implementations • 9 Sep 2022 • Iñigo López-Riobóo Botana, Carlos Eiras-Franco, Julio Hernandez-Castro, Amparo Alonso-Betanzos
EADMNC leverages the formulation of the previous ADMNC model to offer pre hoc and post hoc explainability, while maintaining the accuracy of the original architecture.
no code implementations • 3 May 2022 • Iñigo López-Riobóo Botana, Verónica Bolón-Canedo, Bertha Guijarro-Berdiñas, Amparo Alonso-Betanzos
The items are a subset of users and restaurants and the interactions the reviews posted by these users.
1 code implementation • 14 Dec 2020 • Brais Cancela, Verónica Bolón-Canedo, Amparo Alonso-Betanzos
Classic embedded feature selection algorithms are often divided in two large groups: tree-based algorithms and lasso variants.
1 code implementation • 30 Apr 2019 • Brais Cancela, Verónica Bolón-Canedo, Amparo Alonso-Betanzos, João Gama
Classic feature selection techniques remove those features that are either irrelevant or redundant, achieving a subset of relevant features that help to provide a better knowledge extraction.
no code implementations • 31 Jan 2019 • Raul-Jose Palma-Mendoza, Luis de-Marcos, Daniel Rodriguez, Amparo Alonso-Betanzos
CFS (Correlation-Based Feature Selection) is an FS algorithm that has been successfully applied to classification problems in many domains.
1 code implementation • IEEE 2017 2017 • Sergio Ramírez-Gallego, Héctor Mouriño-Talín, David Martínez-Rego, Verónica Bolón-Canedo, José Manuel Benítez, Amparo Alonso-Betanzos, Francisco Herrera
With the advent of extremely high dimensional datasets, dimensionality reduction techniques are becoming mandatory.
no code implementations • 13 Oct 2016 • Sergio Ramírez-Gallego, Héctor Mouriño-Talín, David Martínez-Rego, Verónica Bolón-Canedo, José Manuel Benítez, Amparo Alonso-Betanzos, Francisco Herrera
With the advent of extremely high dimensional datasets, dimensionality reduction techniques are becoming mandatory.