Article: FESSARec: explaining course recommendations using fuzzy expert system and self-attention Journal: International Journal of Data Analysis Techniques and Strategies (IJDATS) 2024 Vol.16 No.2 pp.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. Inderscience Publishers - linking academia, business and industry through research

Title: FESSARec: explaining course recommendations using fuzzy expert system and self-attention

Authors: Mehbooba P. Shareef; Babita Roslind; Jimson Mathew

Addresses: Division of Electronics, Cochin University of Science and Technology, Kerala, 682022, India; Department of Computer Science and Engineering, Rajagiri School of Engineering and Technology, Kerala, India ' Division of Electronics, Cochin University of Science and Technology, Kerala, 682022, India ' Department of Computer Science and Engineering, Indian Institute of Technology, Bihar, 801106, India

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.

DOI: 10.1504/IJDATS.2024.137870

International Journal of Data Analysis Techniques and Strategies, 2024 Vol.16 No.2, pp.207 - 221

Received: 07 Sep 2023
Accepted: 16 Jan 2024
Published online: 05 Apr 2024 *

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Title: FESSARec: explaining course recommendations using fuzzy expert system and self-attention

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