Authors:
Sisay Adugna Chala
;
Fazel Ansari
and
Madjid Fathi
Affiliation:
University of Siegen, Germany
Keyword(s):
Bidirectional Matching, Job Vacancy, Job Description, Text Mining, LSA, Latent Semantic Analysis.
Related
Ontology
Subjects/Areas/Topics:
Enterprise Information Systems
;
Recommendation Systems
;
Software Agents and Internet Computing
Abstract:
There is a huge online data about job descriptions which has been entered by job seekers and job holders that
can be utilized to give insight into the current state of jobs. Employers also produce large volume of vacancy
data online which can be exploited to portray the current demand of the job market. When preparing job
vacancies, taking into account the information contained in job descriptions, and vice versa, the likelihood of
getting the bidirectional match of a job description and a vacancy will be improved. To improve the quality
of job descriptions and job vacancies, a mediating system is required that connects and supports job designers
and employers, respectively. In this paper, we propose a framework of an automatic bidirectional matching
system that measures the degree of semantic similarity of job descriptions provided by job-seeker, job-holder
or job-designer against the vacancy provided by employer or job-agent. The system provides suggestions to
improve both job desc
riptions and vacancies using a combination of text mining methods.
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