 FESSARec: explaining course recommendations using fuzzy expert system and self-attentionMehbooba P. Shareef, Babita Roslind and Jimson Mathew International Journal of Data Analysis Techniques and Strategies, 2024, vol. 16, issue 2, 207-221 Abstract: Recommendations generated by a model become more convincing when the system is capable of explaining the rationale behind the recommendations with respect to various decision parameters involved. A recommendation system which uses fuzzy expert system and self attention (FESSARec) to explain the recommendations is proposed here. The self-attention module extracts features of learners and courses and generates attention weights which will be used to explain the recommendations. The fuzzy expert system extracts relevant rules from the additional domain knowledge available in the datasets. As a result of this hybrid approach, FESSARec outperforms the recent architectures with which it is compared and obtains a very small root mean square error (RMSE) score of 0.65. FESSARec is also capable of producing top-N recommendations with a very high NDCG of 0.89 and HR of 0.72. It outperforms the best e-commerce baseline by 8% and the educational baseline by 16% of lower error rates. Keywords: E-learning; MOOC; massive open online course; educational recommendation system; explainable recommendation system; self-attention-based recommendation; fuzzy expert systems. (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:injdan:v:16:y:2024:i:2:p:207-221 Access Statistics for this article More articles in International Journal of Data Analysis Techniques and Strategies 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.