Social Network Recommender Systema Neural Network Approach

  1. Alberto Rivas 12
  2. Pablo Chamoso 12
  3. Alfonso González-Briones 12
  4. Juan Pavón 3
  5. Corchado, Juan M. 1245
  1. 1 Universidad de Salamanca

    Universidad de Salamanca

    Salamanca, España


  2. 2 AIR Institute

    AIR Institute

    Carbajosa de la Sagrada, España

  3. 3 Universidad Complutense de Madrid

    Universidad Complutense de Madrid

    Madrid, España

    ROR 02p0gd045

  4. 4 Osaka Institute of Technology

    Osaka Institute of Technology

    Osaka, Japón


  5. 5 Universiti Malaysia Kelantan

    Universiti Malaysia Kelantan

    Kota Bharu, Malasia


Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference: Guimarães, Portugal; November 4–6, 2020. Proceedings
  1. Cesar Analide (ed. lit.)
  2. Paulo Novais (ed. lit.)
  3. David Camacho (ed. lit.)
  4. Hujun Yin (ed. lit.)

Editorial: Springer International Publishing AG

ISBN: 978-3-030-62362-3 978-3-030-62361-6 978-3-030-62364-7 978-3-030-62365-4

Año de publicación: 2020

Título del volumen: Part II

Volumen: 2

Páginas: 213-222

Congreso: Intelligent Data Engineering and Automated Learning – IDEAL (21. 2020. Guimarães)

Tipo: Aportación congreso


Social networks have increased considerably due to the development of networks with specific purposes and represent a high percentage of daily communications between people. Due to the large amount of content in any type of social network, it is necessary to guide users to find the content that best suits their needs. The inclusion of artificial intelligence techniques greatly facilitates the task of finding relevant content. This document presents a recommendation system (RS) for a business and employment-oriented social network. Therefore, job offers are recommended to users, but other users are also encouraged to follow them. The system presented is based on virtual agent organizations, and uses artificial neural networks to determine whether job offers and users should be recommended or not. The system has been evaluated on a real social network and has provided a high acceptance rate of both job offers and user recommendations.