USING STANDARD METRICS TO ASSESS THE QUALITY OF RECOMMENDATIONS WHEN IMPLEMENTING A NEURAL NETWORK INTO A PLATFORM FOR RECOMMENDING SELECTED EDUCATIONAL COMPONENTS
Keywords:
deep learning, recommender system, neural networks, personalized learningAbstract
The paper presents a neural network-based approach to building a personalized recommendation system for elective academic components. The proposed model combines embeddings of students and courses, capturing latent features such as interests, difficulty levels, and content categories. A multi-layer neural architecture processes student-course pairs to estimate the probability of student interest. The system is trained using historical selection data and optimized with a binary cross-entropy or ranking loss. Evaluation uses standard Top-N recommendation metrics, including Precision@10, Recall@10, Mean Average Precision (MAP), and NDCG@10. Experimental results show significantly improved recommendation accuracy compared to classical collaborative and content-based filtering methods.
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