Abstract

The digital era has brought about a myriad of challenges in the job market, where job seekers often struggle to find positions that align with their skills and preferences, while employers face difficulties in identifying the most suitable candidates for their job openings. Existing job recommendation systems, although advanced, often lack the precision and personalization needed to address these challenges effectively. This research paper addresses these issues by proposing an progressive approach that leverages the Heterogeneous Information Network-based GraphSAGE (HINSAGE) algorithm within knowledge graphs. By harnessing the rich semantic and structural information present in heterogeneous information networks (HINs), the study aims to improve job recommendation accuracy and personalization, ultimately benefiting both job seekers and employers by facilitating better job matches and streamlining the recruitment process in the digital job market.