Personalized Recommendation Method for Tourist Attractions Based on User Information Mixed Filtering

In order to improve the effectiveness of tourist attraction recommendation, this article proposes a personalized recommendation method for tourist attractions based on mixed filtering of tourist information. This method includes two parts: the construction of a tourist attraction information database and the personalized recommendation method for tourist attractions. Among them, the construction of the tourist attraction information database includes three steps: mining tourist attraction information based on association rules, updating tourist attraction data, and constructing a tourist attraction information feature vocabulary based on topic similarity clustering. The personalized recommendation methods for tourist attractions mainly include two aspects: describing the semantic association of tourist attraction information and selecting the optimal personalized recommendation path for tourist attraction information. The experimental results show that the proposed method improves the accuracy and efficiency of personalized recommendation for tourist attractions.