DC Field | Value | Language |
dc.contributor.author | BENKHELIFA, ISlam | - |
dc.contributor.author | MECHEREF, ADel Youcef | - |
dc.date.accessioned | 2022-04-12T11:37:40Z | - |
dc.date.available | 2022-04-12T11:37:40Z | - |
dc.date.issued | 2020 | - |
dc.identifier.uri | https://repository.esi-sba.dz/jspui/handle/123456789/180 | - |
dc.description | M. KESKES Nabil Encadreur Mlle. BENNABI Sakina Rim Encadreur | en_US |
dc.description.abstract | All businesses use several techniques in order to maximize pro ts within a reasonable
amount of time. So it is better to do things as quickly as possible. Recommender sys-
tems are widely used systems that provide real-time recommendations of relevant items
to each user. Job recommendation systems play a key role in helping employees nd
the right opportunities and recruiters reaching top candidates. To do so, recommenda-
tions techniques such as collaborative ltering, content-based ltering, knowledge-based
and hybrid approaches can be applied. In this paper, job recommendation systems are
presented into two types, the traditional job recommendation systems that focus on a
single way of recommendation and the reciprocal job recommendation systems that help
to match jobs to employees and employees to jobs. | en_US |
dc.language.iso | en | en_US |
dc.subject | Recommendation Systems | en_US |
dc.subject | Job Recommendation | en_US |
dc.subject | Reciprocal Relevance | en_US |
dc.title | Job Recommendation Systems | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | Master
|