7 papers
Deep Variational Models for Collaborative Filtering-based Recommender Systems
Jesús Bobadilla, Fernando Ortega, Abraham Gutiérrez +1
Deep learning provides accurate collaborative filtering models to improve recommender system results. Deep matrix factorization and their related collaborative neural networks are…
Applying Inter-rater Reliability and Agreement in Grounded Theory Studies in Software Engineering
Jessica Díaz, Jorge Pérez, Carolina Gallardo +1
In recent years, the qualitative research on empirical software engineering that applies Grounded Theory is increasing. Grounded Theory (GT) is a technique for developing theory in…
DevOps Team Structures: Characterization and Implications
Daniel López-Fernández, Jessica Díaz, Javier García +2
Context: DevOps can be defined as a cultural movement to improve and accelerate the delivery of business value by making the collaboration between development and operations effect…
On character varieties of singular manifolds
Ángel González-Prieto, Marina Logares
In this paper, we construct a lax monoidal Topological Quantum Field Theory that computes virtual classes, in the Grothendieck ring of algebraic varieties, of -representation va…
Deep Learning feature selection to unhide demographic recommender systems factors
Jesús Bobadilla, Ángel González-Prieto, Fernando Ortega +1
Extracting demographic features from hidden factors is an innovative concept that provides multiple and relevant applications. The matrix factorization model generates factors whic…
DeepFair: Deep Learning for Improving Fairness in Recommender Systems
Jesús Bobadilla, Raúl Lara-Cabrera, Ángel González-Prieto +1
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 difficul…