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.
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Acknowledgments
This work has been supported by project “BeEMP”. ID: RTC-2016-5642-6. Project co-financed with Min. of Economy, Industry and Competitiveness and ERDF funds.
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Rivas, A., Chamoso, P., González-Briones, A., Pavón, J., Corchado, J.M. (2020). Social Network Recommender System, A Neural Network Approach. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12490. Springer, Cham. https://doi.org/10.1007/978-3-030-62365-4_21
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DOI: https://doi.org/10.1007/978-3-030-62365-4_21
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