Handling the crowd avoidance problem in job recommendation systems integrating FoDRA
by Nikolaos D. Almalis; George A. Tsihrintzis; Ioannis Papaioannou
International Journal of Computational Intelligence Studies (IJCISTUDIES), Vol. 9, No. 1/2, 2020

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.

Online publication date: Thu, 09-Apr-2020

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