 Construction of a personalised online learning resource recommendation model based on self-adaptationZhipeng Chang and Kai Liu International Journal of Knowledge-Based Development, 2023, vol. 13, issue 2/3/4, 394-410 Abstract: With the constant advancement of digital technology, the educational paradigm has experienced seismic shifts. Individuals are becoming more comfortable with using network information technology for online learning. This study proposes an adaptive-based personalised online learning resource recommendation system and constructs an adaptive-based generalised matrix factorisation model-long short-term memory (G-LSTM) model. This model combines generalized matrix decomposition with a long short-term memory network, so the fused model can effectively deal with information timing and cold start problems. The results show that in the RN dataset, when K = 10, the hit rate of the G-LSTM model is 80%, and the normalised loss cumulative gain value can reach 0.48. It can be seen that the recommendation model proposed in this study can meet the purpose of recommending online learning resources according to the different needs of users. And it can further promote the development of school education. Keywords: adaptive; online learning; personalisation; recommender system; matrix factorisation; LSTM; long short-term memory. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijkbde:v:13:y:2023:i:2/3/4:p:394-410 Access Statistics for this article More articles in International Journal of Knowledge-Based Development from Inderscience Enterprises Ltd |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.