Research on integrating naive Bayes and collaborative filtering into an online-course recommendation model for universities

Current college online course recommendation systems struggle with cold start, data sparsity, and limited personalisation, reducing recommendation accuracy and user satisfaction.This study proposes a hybrid model combining naive Bayes and collaborative filtering to address these challenges.By integrating course metadata and user behaviour data, the model extracts multi-dimensional features, capturing both static preferences and dynamic behaviours through probabilistic modelling and collaborative filtering.Experiments on data from 25,000 students and 1,000 courses show that the model improves Precision@10 and Recall@10 by 12% and 10.5% respectively, compared to individual models.In cold-start scenarios, it achieves an F1@10 score of 0.35, compared to 0.27 for DNN.Under 98% sparsity, its accuracy degrades only half as much as traditional collaborative filtering.With 2.3 seconds per iteration and a 26.4% increase in click-through rate, the model demonstrates efficiency and effectiveness in personalised online course recommendations.