Case-Based Reasoning and Agent Based Job Offer Recommender System

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.