A Multi-Criteria Analysis and Advanced Comparative Study of Recommendation Systems

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2021 by IJETT Journal
Volume-69 Issue-3
Year of Publication : 2021
Authors : Safia Baali, Ibrahim Hamzane, Hicham Moutachaouik, Abdelaziz Marzak
DOI :  10.14445/22315381/IJETT-V69I3P213


MLA Style: Safia Baali, Ibrahim Hamzane, Hicham Moutachaouik, Abdelaziz Marzak  "A Multi-Criteria Analysis and Advanced Comparative Study of Recommendation Systems" International Journal of Engineering Trends and Technology 69.3(2021):69-75. 

APA Style:Safia Baali, Ibrahim Hamzane, Hicham Moutachaouik, Abdelaziz Marzak. A Multi-Criteria Analysis and Advanced Comparative Study of Recommendation Systems  International Journal of Engineering Trends and Technology, 69(3),69-75.

In order to ensure the performance of delivery, especially in the IT digital services company, we need to affect the right candidate in the right position; in this context, the recruitment process needs to be automatic, subjective, and more accurate. Employers need help to find the right candidate from an of resumes, and many studies have proposed several solutions for recommending a candidate for recruitment and matching between the job offer and cv candidates that exploit text processing and semantics-based techniques. In our research, we aim to present a comparative study between the different approaches used for the matching job and cv candidate; we also proposed a new approach to recommend a potential candidate for a specific work area, our study will be based on an IT service company based in Morocco and aim the automatization of the recruitment process to ensure the assignment of the candidate in the right task and ensure the success of the company, then the customer’s satisfaction.

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Matching Job/Resume; recommendation system; Clustering; TFIDF; KMeans; recruitment.