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DORIS: Personalized course recommendation system based on deep learning.

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This study introduces DORIS, a personalized course recommendation system using DeepFM to address student course selection challenges. DORIS improves recommendation quality by considering student data and course details, outperforming existing methods.

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Area of Science:

  • Artificial Intelligence
  • Educational Technology
  • Computer Science

Background:

  • Students face cognitive overload when selecting courses from vast options.
  • Existing personalized course recommendation systems struggle with scalability, sparsity, and cold start issues, leading to suboptimal recommendations.

Purpose of the Study:

  • To propose a novel personalized course recommendation system, DORIS (Deep PersOnalized couRse RecommendatIon System).
  • To enhance course recommendation quality by leveraging DeepFM and integrating student basic information, interests, and course details.

Main Methods:

  • Developed a personalized course recommendation system named DORIS.
  • Utilized DeepFM (Deep Factorization Machine) as the core deep learning model.
  • Incorporated student demographic data, stated interests, and comprehensive course information.

Main Results:

  • The proposed DORIS method demonstrated superior performance compared to existing recommendation approaches.
  • Experimental results validated the effectiveness of DORIS in selecting appropriate courses for students.

Conclusions:

  • DORIS effectively addresses the limitations of current course recommendation systems.
  • The system offers a promising solution for personalized course selection, enhancing the student experience.