Article: Research on personalised recommendation of tourist attractions based on improved collaborative filtering algorithm Journal: International Journal of Business Intelligence and Data Mining (IJBIDM) 2026 Vol.28 No.9 pp.84 - 96 Abstract: This study proposed a personalised recommendation method for tourist attractions based on improved collaborative filtering algorithm. Firstly, by analysing dimensions such as user interaction frequency and the proportion of mutual friends, a dynamic weight system is established to build a trust network. Secondly, the concepts of path trust threshold and longest propagation distance are introduced to calculate indirect trust values. Finally, in terms of improving the collaborative filtering algorithm, three innovative correction factors, namely common user size, rating weight, and mean deviation, were introduced to optimise the similarity calculation and obtain Top-N results for tourist attraction recommendations. The experimental results show that the accuracy of the method proposed in this paper is consistently maintained at a high level of 0.9, with an average absolute error controlled within the range of 0.115 to 0.124. Inderscience Publishers - linking academia, business and industry through research

Title: Research on personalised recommendation of tourist attractions based on improved collaborative filtering algorithm

Authors: Xiaoyan Wang

Addresses: Department of Economics and Trade, Yongcheng Vocational College, Yongcheng, 476600, China

Abstract: This study proposed a personalised recommendation method for tourist attractions based on improved collaborative filtering algorithm. Firstly, by analysing dimensions such as user interaction frequency and the proportion of mutual friends, a dynamic weight system is established to build a trust network. Secondly, the concepts of path trust threshold and longest propagation distance are introduced to calculate indirect trust values. Finally, in terms of improving the collaborative filtering algorithm, three innovative correction factors, namely common user size, rating weight, and mean deviation, were introduced to optimise the similarity calculation and obtain Top-N results for tourist attraction recommendations. The experimental results show that the accuracy of the method proposed in this paper is consistently maintained at a high level of 0.9, with an average absolute error controlled within the range of 0.115 to 0.124.

Keywords: improved collaborative filtering algorithm; scenic spot; personalised recommendation; trust model; dynamic weight.

DOI: 10.1504/IJBIDM.2026.154231

International Journal of Business Intelligence and Data Mining, 2026 Vol.28 No.9, pp.84 - 96

Received: 13 Nov 2025
Accepted: 26 Feb 2026
Published online: 17 Jun 2026 *

Title: Research on personalised recommendation of tourist attractions based on improved collaborative filtering algorithm

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