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Research on One-stop Intelligent Tourism Planning Based on User Interests -- A Case Study of Beijing
- 1 School of Economics and Management, University of Science and Technology Beijing, Beijing, China
* Author to whom correspondence should be addressed.
Abstract
With the improvement of people’s living standards in the new era, tourism consumption has gradually become a hotspot of popular entertainment. Beijing faces the challenges of high tourist carrying capacity at attractions and uneven distribution of tourism resources. There is a growing need for personalised travel path planning. This study aims to develop a one-stop personalised intelligent recommendation model for tourist attractions in the Beijing area to enhance tourists’ travel experience. By integrating data from mainstream travel websites such as Ctrip, Tongcheng, and Qunar, the paper uses natural language processing (NLP) technology to conduct analyses of online reviews to derive user sentiment and personalisation indicators. The entropy weight method is used to comprehensively consider the user’s personalised travel preferences, combined with the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method to scientifically rank the attractions and select the candidate set. Finally, the path planning algorithm with distance factor is implemented based on a greedy algorithm to optimise the travel path according to the user’s interest and achieve the recommendation of personalised travel routes. The model proposed in this study shows high accuracy and user satisfaction in empirical tests, which strengthens the user information processing support and personalisation needs in the era of big data, and contributes new solutions to the field of travel path recommendation.
Keywords
Travel Personalised Recommendations, Natural Language Processing, Emotional Analysis, Route Planning
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Cite this article
Qin,J. (2025). Research on One-stop Intelligent Tourism Planning Based on User Interests -- A Case Study of Beijing. Theoretical and Natural Science,101,1-13.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of CONF-MPCS 2025 Workshop: Mastering Optimization: Strategies for Maximum Efficiency
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