@article{4612,
  author = {Jiang Jing},
  title = {Recommender System Model for Security Teaching in the Higher Education System},
  journal = {Information Security Education Journal},
  year = {2025},
  volume = {12},
  number = {2},
  doi = {https://doi.org/10.6025/isej/2025/12/2/49-56},
  url = {https://www.dline.info/isej/fulltext/v12n2/isejv12n2_2.pdf},
  abstract = {The paper proposes an intelligent secure education system tailored for college students using personalized
recommendation technology. Recognizing the growing security challenges students face ranging from
cybersecurity and traffic safety to mental health the system aims to enhance security awareness through
customized learning content. It integrates user management, resource management, recommendation
algorithms, and user interfaces into a cohesive architecture. Central to the system is a matrix factorization
algorithm enhanced with techniques like multi head self attention and feed forward neural networks to
analyze user behavior and recommend relevant educational resources. The system preprocesses diverse
resource types (text, images, video) and extracts features using methods such as word embeddings and
convolutional neural networks. Experimental evaluation compares an experimental group using the
personalized system against a control group using traditional methods. Metrics like recommendation
accuracy, recall, and user satisfaction are analyzed, with results indicating improved learning effectiveness
and engagement. The figures in the paper illustrate the impact of parameters, such as K, on recommendation
performance and compare algorithm accuracy using metrics such as MAE. The study concludes that personalized
recommendation systems significantly enhance secure education by aligning content with individual
student needs, and it calls for further refinement through user feedback and advanced algorithms to address
evolving security education demands.},
}