Handling the crowd avoidance problem in job recommendation systems integrating FoDRA. (2nd April 2020)

• Record Type: Journal Article
• Authors: Almalis, Nikolaos D.; Tsihrintzis, George A.; Papaioannou, Ioannis
• Is Part Of: International journal of computational intelligence studies. Volume 9:Number 1/2(2020)
• Page Start: 128
• Page End: 145
• Publication Date: 2020-04-02
• Subjects: recommender system -- job seeking and recruiting -- job recommender -- information filtering; Computational intelligence -- Periodicals; 006.305
• Languages: English
• ISSNs: 1755-4985

Abstract

In this article, we present the basic principles and approaches of job recommender systems (JRSs). Furthermore, we describe the four different relation types of the job seeking and recruiting problem, derived directly from the formal definition of JRSs. We use our previously published four dimensions recommendation algorithm (FoDRA) to calculate the suitability of a person for a job and then we model a job seeking and recruiting problem consisting of many candidates and many jobs (N-N case). Finally, we test the algorithm and present the results proposing a solution - the minimum acceptable suitability level - for the crowd avoidance problem that occurs. Our study produces good results and shows that this approach can be considered as an important asset in the domain of job seeking and recruiting.