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KLECA: knowledge-level-evolution and category-aware personalized knowledge recommendation

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Abstract

Knowledge recommendation plays a crucial role in online learning platforms. It aims to optimize the service quality so as to improve users’ learning efficiency and outcomes. Existing approaches generally leverage RNN-based methods in combination with attention mechanisms to learn user preference. There is a lack of in-depth understanding of users’ knowledge-level changes over time and the impact of knowledge item categories on recommendation performance. To this end, we propose the knowledge-level-evolution and category-aware personalized knowledge recommendation (KLECA) model. The model firstly leverages bidirectional GRU and the time adjustment function to understand users’ learning evolution by analyzing their learning trajectory data. Secondly, it considers the effect of item categories and descriptive information and enhances the accuracy of knowledge recommendation by introducing a cross-head decorrelation module to capture the information of knowledge items based on a multi-head attention mechanism. In addition, a personalized attention mechanism and gated function are introduced to grab the relationship between items, item categories and user learning trajectory to strengthen the representation of information. Through extensive experiments on real-world data collected from an online learning platform, the proposed approach has been shown to significantly outperform other approaches.

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  1. http://nlp.stanford.edu/data/glove.840b.300d.zip.

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Acknowledgements

This work is supported by the Key Research and Development Plan of Shandong Province (Major Scientific and Technological Innovation Project) (2021CXGC010103).

Funding

This work is supported by the Key Research and Development Plan of Shandong Province (Major Scientific and Technological Innovation Project) (2021CXGC010103).

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Authors and Affiliations

  1. School of Software, Shandong University, Jinan, 250101, Asia, China

    Lin Cheng, Yuliang Shi, Lin Li, Xinjun Wang & Zhongmin Yan

  2. Dareway Software Co., Ltd, Jinan, 250102, Asia, China

    Yuliang Shi

  3. Joint SDU-NTU Centre for Artificial Intelligence Research, Shandong University, Jinan, 250101, Asia, China

    Han Yu

  4. School of Computer Science and Engineering, Nanyang Technological University, Singapore, 639798, Asia, Singapore

    Han Yu

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  1. Lin Cheng
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  4. Han Yu
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  5. Xinjun Wang
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Contributions

LC and YS contributed to the conception of the study; LC performed the experiment; LC and HY contributed to analysis and manuscript preparation; LC and LL performed the data analyses and wrote the manuscript; XW and ZY helped perform the analysis with constructive discussions.

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Correspondence to Yuliang Shi.

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Cheng, L., Shi, Y., Li, L. et al. KLECA: knowledge-level-evolution and category-aware personalized knowledge recommendation. Knowl Inf Syst 65, 1045–1065 (2023). https://doi.org/10.1007/s10115-022-01789-z

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