@article{4459,
  author = {Jiang Jing},
  title = {Personalized Recommendation of Educational Resources Based on K-Means Clustering},
  journal = {Journal of Information Technology Review},
  year = {2025},
  volume = {16},
  number = {2},
  doi = {https://doi.org/10.6025/jitr/2025/16/2/63-70},
  url = {https://www.dline.info/jitr/fulltext/v16n2/jitrv16n2_3.pdf},
  abstract = {In today’s society, the moral development of college students is crucial for maintaining social stability and
growth, making it an urgent issue to address. To provide more practical information on moral development,
we utilize K-Means clustering technology to group users based on their preferences, enabling the provision
of more precise and valuable information. Firstly, the data is classified through a collaborative filtering
algorithm to ensure data comparability and determine user preferences. Based on this, an effective model for
recommending educational resources is created. Subsequently, the K-Means clustering algorithm is employed
to develop a targeted recommendation process based on the specified objective function, yielding an
effective recommendation of educational resources. Through experiments, we have found that this approach
not only aligns well with the content of moral courses but also ensures that students are more engaged and
achieve good results in reality.},
}