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A Personalized Course Recommendation Model Integrating Multi-granularity Sessions and Multi-type Interests

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Abstract

The open online course (MOOC) platform has seen an increase in usage, and there are a growing number of courses accessible for people to select. An effective method is urgently needed to recommend personalized courses for users. Although the existing course recommendation models consider that users' interests change over time, they often model users' learning records as a single time-granularity sequence and ignore the collaboration between different time-granularity sessions when recommending courses. In addition, most course recommendation models tend to use the deep network, which weakens the memory ability of the model. Few methods simultaneously consider long and short-term interests and individual course interests in the latest session, which results in a decline in model performance. To resolve these problems, we design an innovative personalized course recommendation model that Integrating Multi-granularity Sessions and Multi-type Interests (IMSMI), which converts user-course interaction sequences as multi-granularity sessions and uses different types of attention mechanisms to capture multi-type interests. Meanwhile, we introduce the residual connections to further strengthen the memory capability of IMSMI. Experimental results using the XuetangX dataset available to the public demonstrate that IMSMI significantly surpasses other competing models on evaluation metrics. Compared to the next best model, Recall@3 is increased by 20.50%, and MRR@3 is increased by 18.07%.

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Data availability

The MOOCCourse data that support the findings of this study are available from http://moocdata.cn/data/course-recommendation.

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Acknowledgements

This work is supported by National Natural Science Foundation of China (61872168), Jiangsu Education Science "14th Five Year Plan" project (d/2021/01/112), Jiangsu normal university scientific research and practical innovation project (2022XKT1544).

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Authors and Affiliations

  1. School of Computer Science and Technology, Jiangsu Normal University, Xuzhou, 221116, Jiangsu, China

    Yuan Liu, Yongquan Dong, Chan Yin, Cheng Chen & Rui Jia

  2. Xuzhou Cloud Computing Engineering Technology Research Center, Xuzhou, 221116, Jiangsu, China

    Yuan Liu & Yongquan Dong

  3. Jiangsu Engineering Technology Research Center of ICT in Education, Xuzhou, 221116, Jiangsu, China

    Yongquan Dong

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  1. Yuan Liu
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  2. Yongquan Dong
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Contributions

Yuan Liu is mainly responsible for writing and revising papers, experimental design, coding verification, and data analysis.

Yongquan Dong is mainly responsible for determining research ideas and improving data analysis, design, experiment, and full-text writing.

Chan Yin is mainly responsible for data analysis, discussion, and improvement of the experimental scheme.

Cheng Chen is mainly responsible for the discussion and improvement of the overall idea of the paper.

Rui Jia is mainly responsible for the discussion and improvement of the overall idea of the paper.

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Correspondence to Yongquan Dong.

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Liu, Y., Dong, Y., Yin, C. et al. A Personalized Course Recommendation Model Integrating Multi-granularity Sessions and Multi-type Interests. Educ Inf Technol 29, 5879–5901 (2024). https://doi.org/10.1007/s10639-023-12028-5

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