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 i…