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A Course Recommendation Method Based on the Integration of Curriculum Knowledge Graph and Collaborative Filtering Cover

A Course Recommendation Method Based on the Integration of Curriculum Knowledge Graph and Collaborative Filtering

By: Jingyi Hu and  Qingqing Wang  
Open Access
|Jun 2025

Figures & Tables

Figure 1.

RippleNet Model Diagram.
RippleNet Model Diagram.

Figure 2.

Flowchart of the Integrated Recommendation Algorithm
Flowchart of the Integrated Recommendation Algorithm

Figure 3.

Partial View of the Knowledge Graph
Partial View of the Knowledge Graph

Figure 4.

RippleNet-CF Results Chart
RippleNet-CF Results Chart

Figure 5.

Accuracy Results Chart
Accuracy Results Chart

Figure 6.

Recall Results Chart
Recall Results Chart

Figure 7.

F1 Score Chart
F1 Score Chart

COURSE RATING

RatingScore
S<0.21
0.2≤S<0.42
0.4≤S<0.63
0.6≤S<0.84
S≥0.85

TERNARY ENTITY RELATIONSHIPS

EntityRelationshipEntity
Course NameTaught byTeacher
Course NamePublished bySpecific Publisher
Course NameTimeSpecific Publication Time
Course NameBelongs toSpecific Category
Course NameContainsKnowledge Points

EXTRACTION OF SOME COURSE ENTITIES

Course NameEntity
Popular Java FrameworkTsinghua University Press, October 2018, Lectured by Li Lian, Knowledge Points, Computer
Data StructuresPeople's Publishing House, February 2022, Yu Yun, Knowledge Points, Computer
Database PrinciplesPosts and Telecommunications Press, October 2018, Cao Lan, Knowledge Points, Computer
Advanced MathematicsTsinghua University Press, September 2018, Zhang Yu, Knowledge Points, Mathematics
Language: English
Page range: 94 - 100
Published on: Jun 16, 2025
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2025 Jingyi Hu, Qingqing Wang, published by Xi’an Technological University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.