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  4. Vol 6, Issue 1, 2025

Research on Learning Resource Recommendation Based on Latent Factor Model

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DOI: 10.23977/infkm.2025.060108 | Downloads: 7 | Views: 138

Author(s)

Juan Wang 1,2, Lida An 1

Affiliation(s)

1 College of Computer Science, Haojing College of Shaanxi University of Science & Technology, Xi'an, Shaanxi, China
2 Faculity of Electronic Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China

Corresponding Author

Juan Wang

ABSTRACT

Research on personalized learning supported by recommendation technologies must address challenges such as acquiring authentic educational datasets and adapting these technologies across different domains. This paper adopts the Latent Factor Model in the real dataset, which is collected from the intelligent tutoring system Cognitive Tutors, to achieve personalized exercise resource recommendations. This study implements the latent factor model recommendation for learning materials with Python. In addition to achieving personalized recommendations for educational content, the study also explore the theoretical foundation, motivation, and feasibility of domain-transferable algorithm design to provide a reference for subsequent researchers to conduct related comparative experiments and optimization of our algorithm. Moreover, the study hope that this exploration can enlighten the basic theoretical research on the application of recommendation algorithms in the field of education. This exploration work will also motivate research and development in personalized education technology; The study believe that more advanced computational models have the potential to improve educational practices and learner experiences and achieve effective and scalable personalized learning.

KEYWORDS

Latent Factor Model; Learning Resource Recommendation; Cognitive Tutors

CITE THIS PAPER

Juan Wang, Lida An, Research on Learning Resource Recommendation Based on Latent Factor Model. Information and Knowledge Management (2025) Vol. 6: 49-56. DOI: http://dx.doi.org/10.23977/infkm.2025.060108.

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