Explainable Job-Posting Recommendations Using Knowledge Graphs and Named Entity Recognition

The growth of online job-posting repositories provided job-seekers with access to a large number of potential jobs. User assessment of recommended jobs becomes especially a tedious and time-consuming task with the overwhelming number of job recommendations. To enhance the job-seeker’s ability to evaluate the suitability of a recommended job, we propose an explainable job recommendation system, which matches the user to the most relevant jobs based on their profile. Then, the system explains to the user why each job-posting has been recommended to them. The proposed system uses a knowledge graph (KG) structure to model job-postings and user profiles in one homogeneous structure. Graph relations between the job-seekers and job-postings are mined through natural language processing (NLP) of the textual content from job-postings and user-profiles. Based on the graph structure itself and a customized named entity classifier, a human-readable explanation is generated for each recommendation and provided to the job-seeker. The explanation includes information about the matching factors that led the system to recommend a certain job-posting to the user. The proposed system is implemented and tested on a sample data-set of user profiles and job-postings from open online repositories. We use BELU and Rouge-L scores to show that the proposed systems generated relevant explanations for recommended jobs.