Research on knowledge graph model of diversified online resources and personalized recommendation
Zhenglin Ni, Fangwei Ni · Journal of Physics Conference Series · 2020
Abstract This paper proposes an improved knowledge graph model to match the distinctiveness in the education field, considering the lack of robustness and resource diversity in the existing research on knowledge graphs in the education field. A recommendation algorithm based on personalized features is given on the new knowledge graph model to improve recall and precision. Finally, we verify the robustness and expression ability in course resource diversity. The results show that the recall rate of curriculum resources is improved to 0.95-0.96, personalized satisfaction is up to 0.96 and the difference of recommended resources is up to 0.9.