Social Network Recommender System: a Neural Network Approach
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Alberto Rivas [1] [2] ; Pablo Chamoso [1] [2] ; Alfonso González-Briones [1] [2] ; Juan Pavón [3] ; Corchado, Juan M. [1] [2] [4] [5]
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[1] Universidad de Salamanca
Universidad de Salamanca
Salamanca, España
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[2] AIR Institute
AIR Institute
Carbajosa de la Sagrada, España
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[3] Universidad Complutense de Madrid
Universidad Complutense de Madrid
Madrid, España
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[4] Osaka Institute of Technology
Osaka Institute of Technology
Kita Ku, Japón
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[5] Universiti Malaysia Kelantan
Universiti Malaysia Kelantan
Malasia
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- Localización: Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference: Guimarães, Portugal; November 4–6, 2020. Proceedings / Cesar Analide (ed. lit.), Paulo Novais (ed. lit.)
, David Camacho Fernández (ed. lit.) , Hujun Yin (ed. lit.), Vol. 2, 2020 (Part II), ISBN 978-3-030-62365-4, págs. 213-222 - Idioma: inglés
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Resumen
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.
