Fused Attentive Generative Adversarial Network based Personalized English Learning Material Recommendation System utilizing Knowledge Graph

Abstract

The information era has arrived swiftly in the world. Because it is the universal language, English influences how information is transmitted and exchanged throughout the world. Learning English is like having a valuable instrument for learning priceless knowledge. For this reason, it is imperative that English instruction be improved. The Fused Attentive Generative Adversarial Network based Personalized English Learning Material Recommendation System utilizing Knowledge Graph. Initially, input data are collected, given to preprocessing method. In preprocessing remove data redundancy using Generalized Correntropy Sparse Gauss–Hermite Quadrature Filter (GCSG-HQF). In pre-processed output fed to Recommendation System. Here, FAGAN is used to recommend English Learning Material. In general, FAGAN classifier does not express any optimization adaption approaches for determining optimum parameters to assure the Knowledge Graph. Here Clouded Leopard Optimization Algorithm utilized to optimize FAGAN classifier, for Personalized English Learning Material Recommendation System. In Clouded Leopard Optimization Algorithm utilized to optimise weight parameter of the FAGAN. With the use of performance metrics likes accuracy, precision, sensitivity, specificity, F1-score, ROC, computational time are analyzed. The proposed FAGAN-ELMRS-CLOA method attains 30.58%, 28.73% and 35.62%, higher accuracy, 20.48%, 24.73%, 29.32% higher computational time and 30.98%, 26.66% and 21.32% higher ROC analysed with existing models such as personalized English learning material recommendation system depend on knowledge graph (CNN-ELMRS-KG), semantic method for document classification utilizing deep neural networks with multimedia knowledge graph (DNN- ELMRS-MKG), and personalized recommendation of English learning depend on knowledge graph with graph convolutional network (PR- ELMRS-GCN) respectively.