• Mi Re-Unir
    Búsqueda Avanzada
    JavaScript is disabled for your browser. Some features of this site may not work without it.
    Ver ítem 
    •   Inicio
    • UNIR REVISTAS
    • Revista IJIMAI
    • 2020
    • vol. 6, nº 2, june 2020
    • Ver ítem
    •   Inicio
    • UNIR REVISTAS
    • Revista IJIMAI
    • 2020
    • vol. 6, nº 2, june 2020
    • Ver ítem

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

    Autor: 
    Bobadilla, Jesús
    ;
    Gutiérrez, Abraham
    ;
    Alonso, Santiago
    ;
    Hurtado, Remigio
    Fecha: 
    06/2020
    Palabra clave: 
    recommendation systems; clustering; collaborative filtering; dimensionality reduction; group recommendation; IJIMAI
    Revista / editorial: 
    International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)
    Tipo de Ítem: 
    article
    URI: 
    https://reunir.unir.net/handle/123456789/12730
    DOI: 
    https://doi.org/10.9781/ijimai.2020.03.002
    Dirección web: 
    https://www.ijimai.org/journal/bibcite/reference/2758
    Open Access
    Resumen:
    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, colleagues, etc. Our proposal deals with large numbers of automatically detected groups. Marketing and electronic commerce are typical targets of large homogenous groups. Large groups present a major difficulty in terms of automatically achieving homogeneity, equilibrated size and accurate recommendations. We provide a method that combines diverse machine learning algorithms in an original way: homogeneous groups are detected by means of a clustering based on hidden factors instead of ratings. Predictions are made using a virtual user model, and virtual users are obtained by performing a hidden factors aggregation. Additionally, this paper selects the most appropriate dimensionality reduction for the explained RS aim. We conduct a set of experiments to catch the maximum cumulative deviation of the ratings information. Results show an improvement on recommendations made to large homogeneous groups. It is also shown the desirability of designing specific methods and algorithms to deal with automatically detected groups.
    Mostrar el registro completo del ítem
    Ficheros en el ítem
    icon
    Nombre: ijimai_6_2_10.pdf
    Tamaño: 1.471Mb
    Formato: application/pdf
    Ver/Abrir
    Este ítem aparece en la(s) siguiente(s) colección(es)
    • vol. 6, nº 2, june 2020

    Estadísticas de uso

    Año
    2012
    2013
    2014
    2015
    2016
    2017
    2018
    2019
    2020
    2021
    2022
    2023
    2024
    2025
    Vistas
    0
    0
    0
    0
    0
    0
    0
    0
    0
    0
    75
    90
    139
    83
    Descargas
    0
    0
    0
    0
    0
    0
    0
    0
    0
    0
    56
    71
    71
    18

    Ítems relacionados

    Mostrando ítems relacionados por Título, autor o materia.

    • 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 ...
    • 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 ...

    Mi cuenta

    AccederRegistrar

    ¿necesitas ayuda?

    Manual de UsuarioContacto: reunir@unir.net

    Listar

    todo Re-UnirComunidades y coleccionesPor fecha de publicaciónAutoresTítulosPalabras claveTipo documentoTipo de accesoEsta colecciónPor fecha de publicaciónAutoresTítulosPalabras claveTipo documentoTipo de acceso






    Aviso Legal Política de Privacidad Política de Cookies Cláusulas legales RGPD
    © UNIR - Universidad Internacional de La Rioja
     
    Aviso Legal Política de Privacidad Política de Cookies Cláusulas legales RGPD
    © UNIR - Universidad Internacional de La Rioja