Advancing explainable MOOC recommendation systems: a morphological operations-based framework on partially ordered neutrosophic fuzzy hypergraphs

Abstract Recommendation systems constitute an integral part of nearly all digital service platforms. However, the common assumption in most recommendation systems in the literature is that similar users will be interested in similar items. This assumption holds only sometimes due to the inherent inhomogeneity of user-item interactions. To address this challenge, we introduce a novel recommendation system that leverages partially ordered neutrosophic hypergraphs to model higher-order relationships among users and items. The partial ordering of nodes enables the system to develop efficient top-N recommendations with very high Normalized Discounted Cumulative Gain (NDCG). Our approach incorporates the morphological operation of dilation, applied to user clusters obtained through fuzzy spectral clustering of the hypergraph, to generate the requisite number of recommendations. Explanations for recommendations are obtained through morphological erosion applied on the dual of the embedded hypergraph. Through rigorous testing in educational and e-commerce domains, it has been proved that our method outperforms state-of-the-art techniques and demonstrates excellent performance for various evaluation parameters. The NDCG value, a measure of ranking quality, surpasses 0.10, and the Hit Ratio (HR) consistently falls within the range of 0.25 to 0.30. The Root Mean Square Error (RMSE) values are minimal, reaching as low as 0.4. These results collectively position our algorithm as a good choice for generating recommendations with proper explanations, making it a promising solution for real-world applications.