Taylor-ChOA: Taylor-Chimp Optimized Random Multimodal Deep Learning-Based Sentiment Classification Model for Course Recommendation

Abstract

Course recommendation is a key for achievement in a student’s academic path. However, it is challenging to appropriately select course content among numerous online education resources, due to the differences in users’ knowledge structures. Therefore, this paper develops a novel sentiment classification approach for recommending the courses using Taylor-chimp Optimization Algorithm enabled Random Multimodal Deep Learning (Taylor ChOA-based RMDL). Here, the proposed Taylor ChOA is newly devised by the combination of the Taylor concept and Chimp Optimization Algorithm (ChOA). Initially, course review is done to find the optimal course, and thereafter feature extraction is performed for extracting the various significant features needed for further processing. Finally, sentiment classification is done using RMDL, which is trained by the proposed optimization algorithm, named ChOA. Thus, the positively reviewed courses are obtained from the classified sentiments for improving the course recommendation procedure. Extensive experiments are conducted using the E-Khool dataset and Coursera course dataset. Empirical results demonstrate that Taylor ChOA-based RMDL model significantly outperforms state-of-the-art methods for course recommendation tasks.

A Course Teacher Recommendation Method Based on an Improved Weighted Bipartite Graph and Slope One

“…Gao et al [19] proposed a personalized course recommendation model based on a convolutional neural network combined with negative sequence pattern mining. Banbhrani et al [20] used the Taylor-chimp optimization algorithm of stochastic multimodal deep learning to recommend courses. Hao et al [21] proposed a meta-relational course recommendation model to help students with different needs effectively recommend courses.…”

Preface to the Special Issue “Natural Language Processing (NLP) and Machine Learning (ML)—Theory and Applications”

“…The paper authored by Banbhrani et al [4] proposes a novel sentiment classification approach, leading to a robust sentiment classification model for recommending courses with Taylor-chimp Optimization Algorithm enabled Random Multimodal Deep Learning (Taylor ChOA-based RMDL). Extensive experiments are conducted using the E-Khool dataset and the Coursera course dataset, with empirical results demonstrating that the proposed Taylor ChOA-based RMDL model significantly outperforms state-of-the-art methods for course recommendation tasks.…”