Building a Machine Learning Algorithm-Based Model to Suggest Tourist Attractions in Response to Travelers' “Slow Life” Requirements

In order to promote the development of personalized recommendation system in the tourism industry, this study adopts a machine learning collaborative filtering algorithm model, and uses data provided by users and data mining technology to reduce the overload of Internet messages and provide customized content recommendations. The construction of tourist attractions recommendation model based on machine learning is 18% better than the traditional algorithm under the demand of tourists' “slow life”. This research can effectively solve the problems of sparse data and cold start. It provides users with more personalized and people-centered travel route recommendations, which significantly enhancing the travel experience.