Job Recommendation based on Job Profile Clustering and Job Seeker Behavior | Zendy

D. Mhamdi | Zendy; Reda Moulouki | Zendy; M. Y. El Ghoumari | Zendy; Mohamed Azzouazi | Zendy; Laila Moussaid | Zendy

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Job Recommendation based on Job Profile Clustering and Job Seeker Behavior

Author(s) -

D. Mhamdi,

Reda Moulouki,

M. Y. El Ghoumari,

Mohamed Azzouazi,

Laila Moussaid

Publication year - 2020

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2020.07.102

Subject(s) - computer science , matching (statistics) , job analysis , seekers , cluster analysis , recommender system , cluster (spacecraft) , job attitude , job performance , information retrieval , artificial intelligence , job satisfaction , psychology , social psychology , mathematics , statistics , political science , law , programming language

This article presents a recommender system that aims to help job seekers to find suitable jobs. First, job offers are collected from job search websites then they are prepared to extract meaningful attributes such as job titles and technical skills. Job offers with common features are grouped into clusters. As job seeker like one job belonging to a cluster, he will probably find other jobs in that cluster that he will like as well. A list of top n recommendations is suggested after matching data from job clusters and job seeker behavior, which consists on user interactions such as applications, likes and rating.

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