Explore the Personalized Resource Recommendation of Educational Learning Platforms: Deep Learning

Abstract

With the development of educational learning platforms, the resources available on them have become increasingly abundant, which has increased the difficulty of personalized resource recommendations. In order to further improve the effect of personalized recommendation, this paper first analyzed the neural collaborative filtering (NeuCF) algorithm and then improved generalized matrix factorization (GMF) to expanded GMF (EGMF). Furthermore, additional user and project information was incorporated into the input to better capture user preferences and further improve personalized recommendation effects. Experiments were performed using data from a massive open online courses (MOOC) platform. The experimental results demonstrated that the improved NeuCF (INeuCF) algorithm outperformed the other algorithms, including the user-collaborative filtering algorithm, in personalized resource recommendation. When the length of the recommendation list was 10, the INeuCF algorithm achieved an F1 value of 0.227 and a normalized discounted cumulative gain (NDCG) value of 0.337. In comparison to the NeuCF algorithm, the EGMF improved the F1 value by 0.008 and the NDCG value by 0.005. Additionally, the incorporation of other information further enhanced the F1 value by 0.01 and the NDCG value by 0.007. These results verify the effectiveness of the proposed improvement to the NeuCF algorithm and suggest that the method can be practically applied to educational learning platforms to achieve more effective personalized resource recommendations.