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The aim is to develop a personalized recommendation method for tourist attractions utilizing an improved collaborative filtering algorithm.

Research on personalised recommendation of tourist attractions based on improved collaborative filtering algorithm

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Randomized trial evaluates accuracy of a personalized recommendation method for tourist attractions, suggesting enhancements in user experience.

The aim is to develop a personalized recommendation method for tourist attractions utilizing an improved collaborative filtering algorithm.
Established a dynamic weight system based on user interaction frequency and mutual friends.
Introduced path trust threshold and longest propagation distance for indirect trust value calculation.
Optimized similarity calculation using common user size, rating weight, and mean deviation factors.
Achieved a recommendation accuracy consistently maintained at 0.9.
Average absolute error controlled between 0.115 and 0.124.

Xiaoyan Wang (2026) studied this question.

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Randomized trial evaluates accuracy of a personalized recommendation method for tourist attractions, suggesting enhancements in user experience.