Research on employment matching based on artificial intelligence and recommendation algorithm

This paper introduces an employment matching algorithm that employs a artificial intelligence to address the limitations in accuracy and personalization inherent in traditional employment matching systems. Initially, an employment knowledge graph is created using artificial intelligence technology, representing the relationships between jobs and job seekers as a graph structure, thereby uncovering underlying information. The HAN model is then applied to incorporate multi-source data fusion, capturing feature information across various semantic metapaths, which enhances both the personalization and accuracy of the matching system. Finally, comparative experiments confirm the effectiveness and superiority of the proposed algorithm. Results indicate that the employment matching system, grounded in the artificial intelligence approach, markedly surpasses traditional methods in both recommendation accuracy and degree of personalization.