Abstract
In recent times, Job seekers has dramatically increased. Hence, it has become very strenuous to search for potential candidates. Recently recommendation systems have become essential in many areas and newly for human resources. As a recruiter they can identifying profiles to be recruited and this through using online professional social networks such as LinkedIn. For this reason, we present, a generic and simple recommendation system to optimize the recruitment process. In this study, To create the matching between offer made by a recruiter and profiles and to identifies the most suitable candidate we need a important dataset size. We use content based recommendation techniques and a distributed big data processing framework (Apache Spark) and machine learning (ML) libraries. We use then a data augmentation algorithm to improve our results. According to the experiment, the use of our method can result a high precision of 0.936.
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Boukari, S., Mechti, S., Faiz, R. (2022). A Recommendation System for Job Providers Using a Big Data Approach. In: Bădică, C., Treur, J., Benslimane, D., Hnatkowska, B., Krótkiewicz, M. (eds) Advances in Computational Collective Intelligence. ICCCI 2022. Communications in Computer and Information Science, vol 1653. Springer, Cham. https://doi.org/10.1007/978-3-031-16210-7_5
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