Authors:
Siwar Ayadi
;
Manel Bensassi
and
Henda Ben Ghezala
Affiliation:
RIADI Lab, National School of Computer Science, Manouba University, Tunisia
Keyword(s):
e-Recruitment, Business Intelligence, Content-based Recommendation, Similarity Measure, Prescriptive Analysis, Machine Learning Algorithms.
Abstract:
Due to the continuous and growing spread of the corona virus worldwide, it is important, especially in the business era, to develop accurate data driven decision-aided system to support business decision-makers in processing, managing large amounts of information in the recruitment process. In this context, e-Recruitment Recommender systems emerged as a decision support systems and aims to help stakeholders in finding items that match their preferences. However, existing solutions do not afford the recruiter to manage the whole process from different points of view. Thus, the main goal of this paper is to build an accurate and generic data driven system based on Business intelligence architecture. The strengths of our proposal lie in the fact that it allows decision makers to (1) consider multiple and heterogeneous data sources, access and manage data in order to generate strategic reports and recommendations at all times (2) combine many similarity’s measure in the recommendation pr
ocess (3) apply prescriptive analysis and machine learning algorithms to offer adapted and efficient recommendations.
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