Research on personalised recommendation method of popular tourist attractions routes based on machine learning Online publication date: Fri, 11-Jun-2021
by Hui Wang
International Journal of Information and Communication Technology (IJICT), Vol. 18, No. 4, 2021
Abstract: 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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Information and Communication Technology (IJICT):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook