Personalized Recommendation Method of Online Education Resources for Tourism Majors Based on Machine Learning

The online learning platform provides new opportunities for tourism majors to obtain information. However, the diversity and universality of learning resources have led to Exponential growth of data, making it difficult for students to find resources that meet their own needs. To address this issue, this study proposes a personalized recommendation method for tourism professional online education resources based on machine learning. This method combines TF-IDF weight and location information weight on the basis of TextRank algorithm to generate user interest labels and attribute labels for tourism professional online education resources, thereby establishing a user interest model and resource attribute description. By optimizing the K-means clustering algorithm using genetic algorithm, the recommended online education resources for tourism majors are divided into different resource groups. Next, calculate the distance between the user interest model and the center of each resource cluster, and select the resource cluster closest to the user interest model as the recommendation result. Finally, by calculating the similarity between resources, the resources in the resource cluster are sorted to generate a personalized recommendation list. The research results indicate that the recommendation method based on machine learning has a good recommendation effect in personalized recommendation of online education resources for tourism majors. This method effectively utilizes user interest models and resource attribute descriptions to provide personalized learning resource recommendations that meet students' needs, thereby optimizing the learning process and improving learning effectiveness.