Course Learning Recommendation System Using Neural Collaborative Filtering
DOI:
10.47709/brilliance.v4i2.4699Keywords:
Journal Paper, Online Learning, Recommendation System, Matrix Factorization, Machine Learning, Big Data, Neural Collaborative FilteringDimension Badge Record
Abstract
The proliferation of e-learning platforms has created a need for sophisticated course recommendation systems. This paper presents an innovative online course recommendation system using Neural Collaborative Filtering (NCF), a deep learning technique designed to surpass traditional methods in accuracy and personalization. Our system employs a hybrid NCF architecture, integrating matrix factorization with multi-layer perceptron to capture complex user-course interactions. The proposed NCF-based recommendation system aims to address key challenges in the e-learning domain, such as diverse user preferences, varying course content, and evolving learning patterns. By leveraging the power of neural networks, our approach seeks to provide more relevant and personalized course suggestions to learners. Our research contributes to the intersection of deep learning and educational technology, offering new insights into how advanced machine learning techniques can be applied to improve online learning experiences. The proposed system has the potential to enhance the quality of course recommendations, leading to more effective learning pathways for users. This work has important implications for e-learning platforms, educational institutions, and lifelong learners navigating the vast landscape of online courses. By improving the match between learners and courses, we aim to increase engagement, completion rates, and overall satisfaction in online education. Future work will explore the long-term impact of such personalized recommendations on learning outcomes and skill development.
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Accepted Date: 2024-09-23
Published Date: 2024-10-11
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