Title: Group popular travel route recommendation method based on dynamic clustering

Authors: Yanhua Guo

Addresses: Department of Economics and Management, Henan Polytechnic Institute, Nanyang 473000, China

Abstract: This paper proposes a new group popular travel route recommendation method based on dynamic clustering. Based on the recommended pattern graph, the decreasing function of tourist interest and the score matrix of interest preference are calculated. And the dynamic clustering method is used to construct the dynamic mining model of group passenger preference data to obtain the tourist preference data. Based on the preference data, the hot areas of tourist routes are divided, and the Markov model is used to calculate the transfer probability of tourist routes, and the final result of tourist route recommendation is obtained. Experimental results show that, compared with traditional recommendation methods, the proposed method has higher recommendation accuracy and efficiency, and the highest recommendation accuracy and efficiency can reach 97% and 98%. Therefore, the proposed method is more effective.

Keywords: dynamic clustering; popular group; tourist route; recommendation method.

DOI: 10.1504/IJICT.2023.134247

International Journal of Information and Communication Technology, 2023 Vol.23 No.3, pp.231 - 241

Received: 29 Jun 2021
Accepted: 09 Aug 2021
Published online: 15 Oct 2023 *

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