Abstract
The large amounts of information that social networks contain, makes it necessary for them to provide guides and aids that improve users’ experience in the system. In addition to search and filtering tools, users should be presented with the content they wish to obtain before they take any action to find it. To be able to recommend content to users, it is necessary to analyse their profiles and determine what type of content they want to view. The present work is focused on an employability oriented social network for which a job offer recommender system is proposed, following the model of a multi-agent system. The recommendation system has a hybrid approach, consisting of a CBR system and an argumentation framework. The CBR system is capable of deciding, on the basis of a series of metrics and similar cases stored in the system, whether a job offer is likely to be recommended to a user. Besides, the argumentation framework extends the system with an argumentation CBR, through which old and similar cases can be obtained from the CBR system. Finally, based on the different solutions proposed by the agents and the experience gained from past cases, a process of discussion among agents is established. Here, a debate is held in which a final decision is reached, giving the best recommendation to the proposed problem.
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Acknowledgments
This work was conducted within the framework of a project with Ref. RTC-2016-5642-6, financed by the Ministry of Economy, Industry and Competitiveness of Spain and the European Regional Development Fund (ERDF). The research of Alfonso González-Briones has been co-financed by the European Social Fund (Operational Programme 2014–2020 for Castilla y León, EDU/128/2015 BOCYL).
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González-Briones, A., Rivas, A., Chamoso, P., Casado-Vara, R., Corchado, J.M. (2019). Case-Based Reasoning and Agent Based Job Offer Recommender System. In: Graña, M., et al. International Joint Conference SOCO’18-CISIS’18-ICEUTE’18. SOCO’18-CISIS’18-ICEUTE’18 2018. Advances in Intelligent Systems and Computing, vol 771. Springer, Cham. https://doi.org/10.1007/978-3-319-94120-2_3
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DOI: https://doi.org/10.1007/978-3-319-94120-2_3
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