Job Recommendation based on Job Profile Clustering and Job Seeker Behavior
| Autor: | Reda Moulouki, M.Y. El Ghoumari, Mohamed Azzouazi, L. Moussaid, D. Mhamdi |
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| Rok vydání: | 2020 |
| Předmět: |
Matching (statistics)
Information retrieval ComputingMilieux_THECOMPUTINGPROFESSION Computer science 0202 electrical engineering electronic engineering information engineering General Earth and Planetary Sciences 020206 networking & telecommunications 020201 artificial intelligence & image processing 02 engineering and technology Recommender system Technical skills Cluster analysis General Environmental Science |
| Zdroj: | FNC/MobiSPC |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2020.07.102 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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