Research on Heterogeneous Information Network Recommendation System for Online Education

This paper explores challenges and solutions in adaptive online education, with a focus on Heterogeneous Information Networks (HIN). Traditional recommendation methods' accuracy issues lead to an emphasis on improving learning resource recommendations. Advanced similarity calculation methods within HIN, addressing bias problems, are discussed. Information recommendation challenges, especially in extraction and utilization stages, are explored, along with proposed theoretical frameworks for online education data analysis. The paper introduces a HIN-based information recommendation framework, utilizing the Random Walk with Restart and in-degree sensitive parameters. Additionally, a HIN long-tail recommendation method using deep neural networks is presented to counteract biases towards mainstream recommendations. The paper concludes by highlighting the potential of the proposed models to improve accuracy and computational efficiency in online education resource recommendations.