Industrial Problem Solution
Development of Mutual Matching Algorithm
2020-06-151. Company introduction
SmartSocial Co., Ltd. was founded in 2012 and is taking the lead in solving social problems by utilizing big data.
Major business areas include resolution of job mismatching, public works, youth employee tomorrow deduction, small business exploration project, youth tomorrow morning deduction operation support, student employment management, etc.
2. Problem Background and Summary
This industrial problem was discovered while commercializing matching algorithms developed by Smart Social and the Industrial Mathematical Innovation Center, and the "scoring algorithm" currently used for matching job seekers with companies does not reflect various information about job experience of job seekers, resulting in a rush of matching algorithms. The goal is to more accurately quantify individual job competency through modeling that reflects various information (period, level, etc.) about job experience of job seekers.
3. Solving Process
Using 'Matching Score Correction Model A', the work-seeker's corporate matching score is corrected by redefining the job competency by reflecting the level and duration of the job experience. This model has allowed job seekers with different job levels and periods to differentiate their matching scores, and some resolution of matching congestion.
'Matching Score Corrective Model B' is a model that redefines job competency by reflecting the period after the work-seeker's experience is cut off, and the company's matching score varies depending on the period of job-experience break. This model is used in conjunction with 'Matching Score Calibration Model A' to address matching congestion
'Matching Score Calibration Model' allows you to achieve cumulative corrected scores for one or more portfolios of job seekers
The career calculation model utilizes the job classification and level of the National Competency Standards (NCS), and confirms the correction effect through data from companies and job seekers who have been crowded.
4. Ripple effects and future plans
It is expected that the new algorithm will be effective in enhancing the reliability and validity of the service by allowing users to explain the matching service realistically and logically by utilizing scores calculated based on individual experience. Currently, corporate matching services have been used to match students with practical companies, and the service can be presented by expanding the user base to include those in office and career-setting women.
1. Company introduction
SmartSocial Co., Ltd. was founded in 2012 and is taking the lead in solving social problems by utilizing big data.
Major business areas include resolution of job mismatching, public works, youth employee tomorrow deduction, small business exploration project, youth tomorrow morning deduction operation support, student employment management, etc.
2. Problem Background and Summary
This industrial problem was discovered while commercializing matching algorithms developed by Smart Social and the Industrial Mathematical Innovation Center, and the "scoring algorithm" currently used for matching job seekers with companies does not reflect various information about job experience of job seekers, resulting in a rush of matching algorithms. The goal is to more accurately quantify individual job competency through modeling that reflects various information (period, level, etc.) about job experience of job seekers.
3. Solving Process
Using 'Matching Score Correction Model A', the work-seeker's corporate matching score is corrected by redefining the job competency by reflecting the level and duration of the job experience. This model has allowed job seekers with different job levels and periods to differentiate their matching scores, and some resolution of matching congestion.
'Matching Score Corrective Model B' is a model that redefines job competency by reflecting the period after the work-seeker's experience is cut off, and the company's matching score varies depending on the period of job-experience break. This model is used in conjunction with 'Matching Score Calibration Model A' to address matching congestion
'Matching Score Calibration Model' allows you to achieve cumulative corrected scores for one or more portfolios of job seekers
The career calculation model utilizes the job classification and level of the National Competency Standards (NCS), and confirms the correction effect through data from companies and job seekers who have been crowded.
4. Ripple effects and future plans
It is expected that the new algorithm will be effective in enhancing the reliability and validity of the service by allowing users to explain the matching service realistically and logically by utilizing scores calculated based on individual experience. Currently, corporate matching services have been used to match students with practical companies, and the service can be presented by expanding the user base to include those in office and career-setting women.