Title: Method for recommending the best sightseeing route for tourist attractions based on machine learning algorithms

Authors: Fengjuan Tian; Weina Pei

Addresses: Tourism College, Turpan Vocational and Technical College, Turpan, 838000, China ' Tourism College, Turpan Vocational and Technical College, Turpan, 838000, China

Abstract: In order to improve the coverage of tourist attractions and user satisfaction, a machine learning algorithm-based method for recommending the optimal tourist route for tourist attractions is proposed. Firstly, based on the popularity of scenic spots, user travel time, and scenic spot travel time, a rating analysis of user needs is conducted to comprehensively evaluate the overall strength of the scenic spot. Secondly, utilising the hidden Markov model in machine learning to process the sequential data of tourist travel behaviour, an optimal sightseeing route recommendation model that fits tourist preferences is constructed. Finally, based on the recommended route values, combined with tourists' interests and time budget, the best sightseeing route is selected. The experimental results show that the method proposed in this paper consistently maintains a coverage rate of over 80% for tourist attractions along the sightseeing route, and consistently maintains a satisfaction rate of over 90% for tourists.

Keywords: machine learning; scenic spot; recommended best sightseeing route; hidden Markov model; HMM.

DOI: 10.1504/IJBIDM.2025.149083

International Journal of Business Intelligence and Data Mining, 2025 Vol.27 No.2/3/4, pp.315 - 328

Received: 08 Feb 2025
Accepted: 12 Jun 2025
Published online: 13 Oct 2025 *

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