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DC Field | Value | Language |
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dc.contributor.author | Dissanayake, K | - |
dc.contributor.author | Mendis, S | - |
dc.contributor.author | Subasinghe, R | - |
dc.contributor.author | Geethanjana, D | - |
dc.contributor.author | Lunugalage, D | - |
dc.contributor.author | Kasthurirathna, D | - |
dc.date.accessioned | 2022-02-09T04:09:10Z | - |
dc.date.available | 2022-02-09T04:09:10Z | - |
dc.date.issued | 2021-12-09 | - |
dc.identifier.citation | K. Dissanayake, S. Mendis, R. Subasinghe, D. Geethanjana, D. Lunugalage and D. Kasthurirathna, "Career Aura – Smart Resume and Employment Recommender," 2021 3rd International Conference on Advancements in Computing (ICAC), 2021, pp. 353-358, doi: 10.1109/ICAC54203.2021.9671212. | en_US |
dc.identifier.isbn | 978-1-6654-0862-2 | - |
dc.identifier.uri | http://rda.sliit.lk/handle/123456789/1041 | - |
dc.description.abstract | Recruitment and Job seeking are two major factors that are directly proportional to each other. Due to the competitive nature of the present world, the process of acquiring the best resource effectively and efficiently has become a challenging aspect for the companies. As a result, modern job portals have become increasingly popular to address the challenges identified in the early recruitment and job search process. The purpose of this research is to introduce an optimal solution to address the ineffective areas identified in the job and recruitment domain which can further enhance the recruitment and job seeking decisions by utilizing deep learning and sentiment analytic approach along with descriptive analysis. The proposed system recommends the relevant job opportunities by omitting the irrelevant job advertisements for job hunters who are interested in the IT job domain while they input their resume to the system and additionally, they can improve their career decisions by adhering to the prediction schemes. Moreover, the system facilitates recruiters to headhunt top talents efficiently once they input job requirements to the system and candidate suggestions are not only made depending on their resume information but also analyzing their LinkedIn endorsements. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartofseries | 2021 3rd International Conference on Advancements in Computing (ICAC);Pages 353-358 | - |
dc.subject | Career Aura – Smart | en_US |
dc.subject | Smart Resume | en_US |
dc.subject | Employment Recommender | en_US |
dc.title | Career Aura–Smart Resume and Employment Recommender | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1109/ICAC54203.2021.9671212 | en_US |
Appears in Collections: | Research Papers - Dept of Computer Science and Software Engineering Research Papers - SLIIT Staff Publications |
Files in This Item:
File | Description | Size | Format | |
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Career_Aura__Smart_Resume_and_Employment_Recommender.pdf Until 2050-12-31 | 1.44 MB | Adobe PDF | View/Open Request a copy |
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