Hybrid job offer recommender system in a social network. Issue 4 (22nd May 2019)

• Record Type: Journal Article
• Authors: Rivas, Alberto; Chamoso, Pablo; González‐Briones, Alfonso; Casado‐Vara, Roberto; Corchado, Juan Manuel
• Other Names: Jiménez Alfredo guestEditor.; Herrero Álvaro guestEditor.
• Is Part Of: Expert systems. Volume 36:Issue 4(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-05-22
• Subjects: agents -- argumentation -- employability -- machine learning -- recommender systems -- social networks; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12416
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Recommender systems (RSs) play a very important role in web navigation, ensuring that the users easily find the information they are looking for. Today's social networks contain a large amount of information and it is necessary that they employ a mechanism that will guide users to the information they are interested in. However, to be able to recommend content according to user preferences, it is necessary to analyse their profiles and determine their preferences. The present work proposes a job offer RS for a career‐oriented social network. The recommendation system is a hybrid, it consists of a case‐based reasoning (CBR) system and an argumentation framework, based on a multi‐agent system (MAS) architecture. The CBR system uses a series of metrics and similar cases to decide 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, a discussion process is established amongst the agents who debate using their experience from past cases to take a final decision.