Research On Tourist Attraction Recommendation Method Based On Weibo Big Data And Machine Learning Algorithm

This paper studies the method of scenic spot recommendation based on microblog Big data and machine learning algorithm. With the rapid development of social media, more and more users are sharing their travel experiences and experiences on Weibo. By utilizing these massive Weibo data, combined with machine learning algorithms, personalized recommendations for tourist attractions can be provided to users, improving their travel experience and satisfaction. This paper introduces the implementation process of scenic spot recommendation method based on microblog Big data and machine learning algorithm in detail. Firstly, by capturing and analyzing Weibo data, users' geographic location, interests, and travel experiences are extracted. Then, machine learning algorithms are used to model and analyze these data, extracting user behavior patterns and preferences. Finally, based on the current status and needs of users, combined with the characteristics of scenic spots and user preferences, personalized recommendations for tourist attractions are made. Through experimental simulation, we can conclude that the algorithm proposed in this paper has higher accuracy than other algorithms.