Recommendation Systems for E-Learning Platforms Using Machine Learning Algorithms
Author
Ravi Kumar, Shailendra Vishwakarma, Keshav Singh Sisodiya, Mohammed Rehan, Shubhra Pandey, Rudraksh Agrawal
Abstract
The rapid expansion of e-learning platforms has resulted in an overwhelming amount of educational content, making it difficult for learners to identify relevant materials. Recommendation systems powered by machine learning provide personalized learning experiences by analyzing user behavior and preferences. This paper proposes a hybrid recommendation system that combines collaborative filtering and content-based filtering techniques to enhance recommendation accuracy. Experimental results show that the hybrid approach outperforms individual models in terms of precision, recall, and user satisfaction. The study demonstrates the effectiveness of machine learning in improving engagement and learning outcomes in e-learning environments.
Keywords
E-learning, Recommendation System, Machine Learning, Collaborative Filtering, Content-Based Filtering, Hybrid Model, Learning Analytics
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References
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