An Application of Text Mining and Association Rule Mining to Job and Skill Recommendations for IT Jobs
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Abstract
This research implemented a web application that employed text mining to extract skill requirements from online IT job announcements, and association rule mining to discover the co-occurrence of hard skills (technical abilities) and soft skills (personal competencies) specified by the jobs. The matching score of each job was calculated by comparing hard skills extracted from the job announcement with a user’s current hard skills. Jobs were recommended to the user based on their matching scores. In addition, the discovered association rules were used to recommend new skills as follows: (1) based on the user’s current hard skills as antecedents, new hard skills as consequences would be recommended; and (2) based on the user’s current hard skills or soft skills as antecedents, new soft skills as consequences would be recommended. Online training courses to obtain such new skills were also recommended. The application was evaluated by 40 users, and received high satisfaction scores on both job recommendation and skill recommendation.
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