Open AccessResearch on knowledge graph model of diversified online resources and personalized recommendation
Author(s) -
Zhenglin Ni,
Fangwei Ni
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1693/1/012178
Subject(s) - optimal distinctiveness theory , computer science , robustness (evolution) , recall , graph , knowledge management , knowledge graph , curriculum , artificial intelligence , theoretical computer science , psychology , pedagogy , biochemistry , chemistry , cognitive psychology , psychotherapist , gene
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.