Title: Personalised recommendation of regional tourism routes based on weighted matrix factorisation

Authors: Guojing Fan; Pan Fan; Qiang Wu; Hui Cai; Lu Chen

Addresses: School of History, Culture and Tourism, Gannan Normal University, Ganzhou, 341000, Jiangxi, China ' College of Data Science, Taiyuan University of Technology, Jinzhong, 030600, Shanxi, China ' School of History, Culture and Tourism, Gannan Normal University, Ganzhou, 341000, Jiangxi, China ' School of History, Culture and Tourism, Gannan Normal University, Ganzhou, 341000, Jiangxi, China ' School of History, Culture and Tourism, Gannan Normal University, Ganzhou, 341000, Jiangxi, China

Abstract: In order to improve the recommendation accuracy and scenic spot coverage of tourism route recommendation methods, this paper proposes a new regional tourism routes personalised recommendation method based on weighted matrix factorisation. Firstly, according to the frequent tourism location sequence, the user tourism preference model is constructed to mine the user tourism preference. Secondly, from the two aspects of influence weight and time weight of tourist attractions, the weighted scoring matrix of tourist attractions is constructed. Finally, the weighted matrix is dimensionally reduced by using the matrix factorisation method, and the prediction scoring matrix is constructed to score the scenic spots in the scenic spots collection, and the top N scenic spots with higher scores are recommended to users as personalised tourist routes. The experimental results show that the accuracy, coverage and prevalence of this method are higher than those of traditional methods, with the highest accuracy of 99.7%.

Keywords: weighting matrix; tourist routes; personalised recommendation; user preferences.

DOI: 10.1504/IJRIS.2025.145078

International Journal of Reasoning-based Intelligent Systems, 2025 Vol.17 No.1, pp.17 - 23

Received: 21 Feb 2023
Accepted: 27 Apr 2023
Published online: 18 Mar 2025 *

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