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Personalized Course Recommendation System Fusing with Knowledge Graph and Collaborative Filtering.

Gongwen Xu1, Guangyu Jia1, Lin Shi1

  • 1School of Business, Shandong Jianzhu University, Jinan 250101, China.

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Summary
This summary is machine-generated.

This study introduces a new personalized course recommendation algorithm that combines knowledge graphs and collaborative filtering. This enhanced approach improves course recommendations by considering semantic relationships, leading to better learner engagement and satisfaction.

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Area of Science:

  • Educational Technology
  • Artificial Intelligence
  • Computer Science

Background:

  • Personalized course recommendation is crucial for online education.
  • Current collaborative filtering algorithms overlook item semantic relationships, limiting recommendation effectiveness.
  • Learner engagement and personalized learning paths are key goals in online education.

Purpose of the Study:

  • To develop an improved recommendation algorithm for online courses.
  • To address the limitations of traditional collaborative filtering by incorporating semantic information.
  • To enhance the performance of personalized course recommendations.

Main Methods:

  • Utilized knowledge graph representation learning to embed item semantic information into a low-dimensional space.
  • Calculated semantic similarity between recommended items.
  • Integrated item semantic information into a collaborative filtering recommendation algorithm.

Main Results:

  • The proposed algorithm demonstrated improved recommendation performance at the semantic level.
  • Achieved higher precision, recall, and F1 scores compared to traditional recommendation algorithms.
  • Effectively recommended courses tailored to individual learners' needs.

Conclusions:

  • The integration of knowledge graphs with collaborative filtering significantly enhances personalized course recommendations.
  • The proposed method offers a more effective approach to understanding and utilizing item semantics in educational recommendation systems.
  • This research contributes to advancing intelligent tutoring systems and adaptive learning environments.