45 citations · 147 across the 11 of their papers we have counts for
3 papers · 1 filter
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…
Providing reliability in Recommender Systems through Bernoulli Matrix Factorization
Fernando Ortega, Raúl Lara-Cabrera, Ángel González-Prieto +1
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…