Listar UNIR REVISTAS por autor 

Listar UNIR REVISTAS por autor "Bobadilla, Jesús"

Mostrando ítems 1-6 de 6

    • A Collaborative Filtering Probabilistic Approach for Recommendation to Large Homogeneous and Automatically Detected Groups 

      Bobadilla, Jesús; Gutiérrez, Abraham; Alonso, Santiago; Hurtado, Remigio (International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), 06/2020)
      In the collaborative filtering recommender systems (CFRS) field, recommendation to group of users is mainly focused on stablished, occasional or random groups. These groups have a little number of users: relatives, friends, ...

    • Classification-based Deep Neural Network Architecture for Collaborative Filtering Recommender Systems 

      Bobadilla, Jesús; Ortega, Fernando; Gutiérrez, Abraham; Alonso, Santiago (International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), 03/2020)
      This paper proposes a scalable and original classification-based deep neural architecture. Its collaborative filtering approach can be generalized to most of the existing recommender systems, since it just operates on the ...

    • Comprehensive Evaluation of Matrix Factorization Models for Collaborative Filtering Recommender Systems 

      Bobadilla, Jesús; Dueñas-Lerín, Jorge; Ortega, Fernando; Gutiérrez, Abraham (International Journal of Interactive Multimedia and Artificial Intelligence, 04/2023)
      Matrix factorization models are the core of current commercial collaborative filtering Recommender Systems. This paper tested six representative matrix factorization models, using four collaborative filtering datasets. ...

    • DeepFair: Deep Learning for Improving Fairness in Recommender Systems 

      Bobadilla, Jesús; Lara-Cabrera, Raúl; González-Prieto, Ángel; Ortega, Fernando (International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), 06/2021)
      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 ...

    • Neural Collaborative Filtering Classification Model to Obtain Prediction Reliabilities 

      Bobadilla, Jesús; Gutiérrez, Abraham; Alonso, Santiago; González-Prieto, Ángel (International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), 06/2022)
      Neural collaborative filtering is the state of art field in the recommender systems area; it provides some models that obtain accurate predictions and recommendations. These models are regression-based, and they just return ...

    • Testing Deep Learning Recommender Systems Models on Synthetic GAN-Generated Datasets 

      Bobadilla, Jesús; Gutiérrez, Abraham (International Journal of Interactive Multimedia and Artificial Intelligence, 10/2023)
      The published method Generative Adversarial Networks for Recommender Systems (GANRS) allows generating data sets for collaborative filtering recommendation systems. The GANRS source code is available along with a representative ...