Interest-based occupation recommendation

Ankhtuya Ochirbat, Timothy K. Shih

研究成果: 雜誌貢獻會議論文同行評審


A mass of adolescents has decided their occupations/jobs/majors out of proper and professional advice from school services. For instance, adolescents do not have adequate information about occupations/jobs, what occupations can be reached by which majors, and what kind of education and training are needed for particular jobs. On the other hand, major choices of adolescents are influenced by a society and their family. They receive occupational information in common jobs from the environment. But they are a lack of information in professional occupations. Furthermore, the choice of major has become increasingly complex due to the existence of multiple human skills, which mean each person has their ability at the certain area and can be applied to multiple jobs/occupations. For those reasons, students need an automatic counselling system according to their values. To do this, occupation recommendation system is implemented with a variety of IT and soft skills. The main goal of this research is to build an occupation recommendation system (ORS) by using data mining and natural language processing (NLP) methods on open educational resource (OER) and skill dataset, in order to help adolescents. The system can provide different variety of academic programs, required skills, ability, knowledge, and job tasks as well as relevant occupational descriptions. The system can assist adolescents in major selection and career planning. Furthermore, the system incorporates a set of searching results, which are recommended using similarity measurements and hybridization recommendation techniques. These methods serve as a base for recommending occupations that meet interests and competencies of adolescents.

期刊Proceedings of Science
出版狀態已出版 - 2018
事件2018 International Symposium on Grids and Clouds in conjunction with Frontiers in Computational Drug Discovery, ISGC 2018 and FCDD - Taipei, Taiwan
持續時間: 16 3月 201823 3月 2018


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