Research on personalized learning path recommendation model of artificial intelligence in new business

Due to the flourishing expansion of online education, students' appetite for individualized learning experiences has significantly intensified. Consequently, we introduce a model that utilizes machine learning to recommend personalized learning paths. The model incorporates several algorithms such as collaborative filtering, content recommendation and reinforcement learning. First, we use collaborative filtering algorithms to analyze students' learning behavior data to identify users with similar interests and academic backgrounds. Then, through the content recommendation algorithm, we dynamically adjust the recommendation results according to the course characteristics and students' learning preferences. Finally, the reinforcement learning algorithm plays a role in optimizing the long-term learning path in the model, and constantly improves the recommendation strategy through real-time feedback. In the experiment, we used multiple real learning platform data sets for validation. The results show that the model has excellent performance in improving students' learning effect and satisfaction, and can effectively recommend learning paths that meet individual needs. Our research provides new ideas and methods for personalized learning of new business education, and promotes the deep integration of education and artificial intelligence.