Improving the efficiency of depersonalized job matching systems using topsis method

Every company occasionally deals with the problem of hiring new staff. The issue of selecting a suitable candidate for a job is often very complicated and many criteria are involved in the selection process. In the case of disadvantaged people, the topic becomes more important since job matching for disadvantaged maintains social inclusion. As a part of our Urban Europe Project entitled "Get together without Barriers" we analyzed women who left their jobs for maternal reasons as disadvantaged people and elicit their requirements from the system via interviews. In this study we propose to improve existing depersonalized job matching platforms with Multi criteria decision making (MCDM) methods. In current depersonalized job matching systems, the applicants' personal information is not shared with the employers but it is reported that it takes too long to find a correct applicant. By embedding MCDM methods in depersonalized systems, job seeker search will be done in a more objective way without any extra search efforts.