An intelligent recommendation strategy for integrated online courses in vocational education based on short-term preferences

Abstract

With the swift advancement of online teaching in vocational education, an increasing number of web-based course materials are being made available to students, granting them the freedom to select resources that suit their personal needs. To optimize the effectiveness of artificial intelligence-enabled smart vocational education, this study presents a course recommendation model centered on learning behaviors and interests. The model utilizes short-term preferences reconstruction behavior contribution to identify fluctuations in learners' interests in real-time. A model for recommending courses is proposed based on short-term preferences and enhancements to learning behavior. Its purpose is to tackle the issue of generalization arising from sparsity and weak correlation in learning behavior. The outcomes demonstrated the model put forth in the study achieved higher Hit Rate (HR) and Normalized Discounted Cumulative Gain (NDCG) values in comparison experiments with multiple models. Hence, this suggested that creating a novel component of historical learning behavior, powered by dynamic interest factors, could resolve the issue of changing learning interests and enhance the efficacy of course recommendation models. Furthermore, the introduction of a correlation mapping network enables the forward mapping transformation from weak to strong learning behavior, thus improving and optimizing input for the agent strategy, reducing data sparsity, and enhancing the performance and generalization of the course recommendation model.