- The Association Rule Recommendation approach outperforms random recommendations by leveraging frequent itemsets and association rules to provide more relevant and personalized course suggestions.
- The effectiveness of the Association Rule Recommendation method relies on having a sufficient number of frequent antecedents and consequents in the data that meet the minimum confidence threshold. For users with less common course histories, recommendations can be generated directly from the frequent itemset mining (FIM) results.
- To address the cold start problem for new users with no prior data, the proposed solution is to recommend beginner and intermediate-level course bundles or pathways identified through FIM, separately for technical and non-technical courses. This approach provides a structured learning foundation tailored to new users' potential needs and experience levels.