Title: A personalised travel route recommendation method based on an improved greedy algorithm

Authors: Qun Shang

Addresses: College of Culture and Tourism, Jiangsu Vocational Institute of Commerce, Nanjing, 211168, China

Abstract: In order to improve the low accuracy, recall rate, and popularity of the traditional method, this paper proposes a personalised tourism route recommendation method based on the improved greedy algorithm. Firstly, the attractiveness rating index of tourist attractions is established, the weight of the index is determined by the normalisation method, and the attractiveness rating is obtained by combining the condition evaluation matrix and potential evaluation matrix. Then, the greedy algorithm is improved by decomsolving the maximal link subgraph to find the biggest node of influence, and the individual contribution value of tourism route resource knowledge is calculated by using the improved greedy algorithm. Finally, personalised tourism route resources are recommended according to different individual contribution values. The results show that the maximum recommendation accuracy of this method can reach 96%, the maximum recall rate can reach 80%, and the maximum popularity is close to 4.8.

Keywords: maximal unicom subgraph; perceptual sorting; greedy decision-making range; attractiveness rating; improved greedy algorithm; personalised recommendation.

DOI: 10.1504/IJCSM.2023.131624

International Journal of Computing Science and Mathematics, 2023 Vol.17 No.4, pp.399 - 413

Received: 31 Aug 2021
Accepted: 21 Jan 2022
Published online: 21 Jun 2023 *

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