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Profiling temporal learning interests with time-aware transformers and knowledge graph for online course recommendation

Profiling users’ temporal learning interests is key to online course recommendation. Previous studies mainly profile users’ learning interests by aggregating their historical behaviors with simple fusing strategies, which fails to capture their temporal interest patterns underlying the sequential us...

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Detalles Bibliográficos
Autores principales: Zhou, Jilei, Jiang, Guanran, Du, Wei, Han, Cong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8891746/
http://dx.doi.org/10.1007/s10660-022-09541-z
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author Zhou, Jilei
Jiang, Guanran
Du, Wei
Han, Cong
author_facet Zhou, Jilei
Jiang, Guanran
Du, Wei
Han, Cong
author_sort Zhou, Jilei
collection PubMed
description Profiling users’ temporal learning interests is key to online course recommendation. Previous studies mainly profile users’ learning interests by aggregating their historical behaviors with simple fusing strategies, which fails to capture their temporal interest patterns underlying the sequential user behaviors. To fill the gap, we devise a recommender that incorporates time-aware Transformers and a knowledge graph to better capture users’ temporal learning interests. First, we introduce stacked Transformers to extract users’ temporal learning interests underlying users’ course enrollment sequences. In addition, we design a time-aware positional encoding module to consider the enrollment time intervals between courses. Third, we incorporate a knowledge graph to utilize the latent knowledge connections between courses. The proposed method outperforms state-of-the-art baselines for course recommendation. Furthermore, findings in the ablation study offers several insights for future research. The proposed model can be implemented in online learning platforms to increase user engagement and reduce dropout rate.
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spelling pubmed-88917462022-03-04 Profiling temporal learning interests with time-aware transformers and knowledge graph for online course recommendation Zhou, Jilei Jiang, Guanran Du, Wei Han, Cong Electron Commer Res Article Profiling users’ temporal learning interests is key to online course recommendation. Previous studies mainly profile users’ learning interests by aggregating their historical behaviors with simple fusing strategies, which fails to capture their temporal interest patterns underlying the sequential user behaviors. To fill the gap, we devise a recommender that incorporates time-aware Transformers and a knowledge graph to better capture users’ temporal learning interests. First, we introduce stacked Transformers to extract users’ temporal learning interests underlying users’ course enrollment sequences. In addition, we design a time-aware positional encoding module to consider the enrollment time intervals between courses. Third, we incorporate a knowledge graph to utilize the latent knowledge connections between courses. The proposed method outperforms state-of-the-art baselines for course recommendation. Furthermore, findings in the ablation study offers several insights for future research. The proposed model can be implemented in online learning platforms to increase user engagement and reduce dropout rate. Springer US 2022-03-03 /pmc/articles/PMC8891746/ http://dx.doi.org/10.1007/s10660-022-09541-z Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Zhou, Jilei
Jiang, Guanran
Du, Wei
Han, Cong
Profiling temporal learning interests with time-aware transformers and knowledge graph for online course recommendation
title Profiling temporal learning interests with time-aware transformers and knowledge graph for online course recommendation
title_full Profiling temporal learning interests with time-aware transformers and knowledge graph for online course recommendation
title_fullStr Profiling temporal learning interests with time-aware transformers and knowledge graph for online course recommendation
title_full_unstemmed Profiling temporal learning interests with time-aware transformers and knowledge graph for online course recommendation
title_short Profiling temporal learning interests with time-aware transformers and knowledge graph for online course recommendation
title_sort profiling temporal learning interests with time-aware transformers and knowledge graph for online course recommendation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8891746/
http://dx.doi.org/10.1007/s10660-022-09541-z
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