A Study on Personalized Learning Resource Recommendation Method Based on Association Rule Mining

As the diversity of educational resources continues to grow, it is becoming increasingly important to provide learners with learning resources that meet their needs and interests. This paper proposes a personalized learning resource recommendation method based on association rule mining, aimed at providing learners with more accurate and personalized learning resource recommendations. Firstly, by analyzing the learners’ historical learning behavior and resource usage, the interest model of the learners is extracted. Then, using association rule mining techniques, potential relationships between learning resources are discovered, generating recommendation rules. Finally, based on the learners’ interest model and recommendation rules, personalized learning resource recommendations are provided for learners. Experimental results show that compared to traditional recommendation methods, this method can significantly improve the accuracy and satisfaction of recommendations.