Research on personalised recommendation method of popular tourist attractions routes based on machine learning

摘要

In view of the problems existing in the current tourist route recommendation methods, this paper proposes a personalised tourist attraction route recommendation method based on machine learning. The tourism heterogeneous information data is collected by using HTML parser and the captured data are used as machine learning training samples. The characteristics and user interest characteristics under the rating were extracted, and the target user interest characteristics were taken as the starting point, combining the features of scenic spots and tourist routes. Recommend scenic spots to users to realise personalised recommendation of popular scenic spots. The experimental results show that the method proposed in this paper has a recommendation accuracy of more than 95%, a recommendation time of between 115 ms-130 ms and a user satisfaction rate of over 89%. With high recommendation accuracy and user satisfaction, and less time to calculate the recommendation, it is a reliable method to recommend tourist routes.