Title: A personalised recommendation method for low-carbon tourism routes based on user feature mining

Authors: Bing Hou

Addresses: Department of Tourism and Hospitality Management, Luohe Vocational Technology College, Luohe, 462000, China

Abstract: In order to overcome the problem of poor user feature mining accuracy and recommendation satisfaction in personalised recommendation methods, a low-carbon tourism route personalised recommendation method based on user feature mining is proposed. Firstly, determine the changes in the cost of low-carbon tourism production behaviour and the demand curve for low-carbon tourism, as well as the characteristics of low-carbon tourism. Secondly, calculate the similarity of low-carbon tourism user features to achieve overall feature mining for low-carbon tourism users. Finally, calculate the user feature entropy and conditional entropy, and analyse the key degree of low-carbon behaviour characteristics of tourism users. Personalise recommendations for low-carbon tourism routes that meet the conditions based on users' preferred low-carbon tourism attractions. The experimental results indicate that the research method can effectively improve the efficiency of fine-grained user feature mining, and the satisfaction with personalised recommendation of low-carbon tourism routes is high.

Keywords: user feature mining; low carbon tourism; route planning; personalised recommendation; conditional entropy; route planning.

DOI: 10.1504/IJRIS.2025.148711

International Journal of Reasoning-based Intelligent Systems, 2025 Vol.17 No.5, pp.301 - 308

Received: 01 Jun 2023
Accepted: 12 Jul 2023
Published online: 21 Sep 2025 *

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