Social Network Recommender Systema Neural Network Approach

  1. Alberto Rivas 12
  2. Pablo Chamoso Santos 12
  3. Alfonso González Briones 12
  4. Juan Luis Pavón Mestras 3
  5. Juan Manuel Corchado Rodríguez 1245
  1. 1 Universidad de Salamanca
    info

    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

  2. 2 AIR Institute
    info

    AIR Institute

    Carbajosa de la Sagrada, España

  3. 3 Universidad Complutense de Madrid
    info

    Universidad Complutense de Madrid

    Madrid, España

    ROR 02p0gd045

  4. 4 Osaka Institute of Technology
    info

    Osaka Institute of Technology

    Osaka, Japón

    ROR https://ror.org/02znffm54

  5. 5 Universiti Malaysia Kelantan
    info

    Universiti Malaysia Kelantan

    Kota Bharu, Malasia

    ROR https://ror.org/0463y2v87

Show affiliations +
Book:
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 Fernández (ed. lit.)
  4. Hujun Yin (ed. lit.)

Publisher: 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

Year of publication: 2020

Volume Title: Part II

Volume: 2

Pages: 213-222

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

Type: Conference paper

DIALNET GOOGLE SCHOLAR lock_openOpen access editor

Abstract

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.

Data source: Dialnet