Personalised Recommendation of Literary Learning Resources Based on a Mixed Recommendation of Learning Interest and Contextual Awareness

Min Guo ()
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Min Guo: School of Humanities and Education, Guangzhou Institute of Science and Technology, Guangzhou 510540, P. R. China

Journal of Information & Knowledge Management (JIKM), 2024, vol. 23, issue 04, 1-19

Abstract: In an effort to improve the efficiency and recommendation accuracy of mobile learning resources, the study proposes a hybrid mobile learning strategy based on Collaborative Filtering (CF), context and interest. Analyse from the perspective of situational awareness, construct a personalised recommendation model for text learning resources based on GimbalTM, and obtain a recommendation form. The experimental results show that the RMSE and MAE of Context-Collaborative filtering (C-CF) are lower than those of traditional CF. The Precision and Recall values of C-CF are higher than those of CF at 10 s, the recommendation growth rates of traditional CF and C-CF are 2.09% and 1.67%, respectively. The Gimbal software enables a certain degree of learner location detection and can trigger contextual rules based on time and location contexts to provide users with personalised text-based learning resources. The research results indicate that in specific applications, over time, under the recommendation system, students’ grades steadily increase, which is also beneficial for improving their learning efficiency.

Keywords: Interest in learning; context awareness; hybrid recommendation; personalised recommendations (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0219649224500552

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