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Personalized Recommendation of Tourist Attractions Based on Collaborative Filtering

Scenic spot recommendation system is one of the main ways for tourist portals to recommend scenic spots to people. As the traditional collaborative filtering recommendation algorithm is a single overall score, it lacks multi-faceted understanding of user interest preferences, which leads to inaccurate recommendation. Therefore, this paper uses questionnaire survey to collect travel preferences of different user attributes, mine user interests based on hierarchical sampling statistical model, and uses AHP to set user attribute weights. Finally, the improved collaborative filtering algorithm is used to establish a personalized tourism recommendation model. The experimental results show that the calculation error of the system is small, and the recommendation effect is good, which has a broad development prospect.