An Enhanced Semantic Analysis of Potential Recommender Systems for Big Data Management

this paper proposes a more desirable semantic analysis for capacity recommender structures for large facts management. The proposed approach is primarily based on ontologies, text-mining, and semantic relatedness processes. The proposed technique uses a site particular ontology to perceive relationships among exceptional objects of the domain, as well as semantically and syntactically analyzing text from literature. The goals of the proposed technique are to: (I) identify the relationships among special objects (places, sports, and attendances; (ii) perceive the effect of each courting on the advice performance; (iii) and discover feasible styles from the relationships. to evaluate the proposed method, the effects are in comparison with current methods. The comparison results display that the proposed technique plays higher than other strategies in both efficiency and accuracy. Moreover, the proposed approach can be applied to other domains, supplying new opportunities in recommendation gadget layout.