A new intelligent method for travel path recommendation based on improved particle swarm optimisation
by Si Han
International Journal of Computing Science and Mathematics (IJCSM), Vol. 12, No. 1, 2020

Abstract: In order to overcome the problem of increasing fluctuation of Intelligent Tourism data and fuzzy optimal solution, the improved particle swarm optimisation algorithm is introduced to design intelligent tourism path recommendation method. The gray Markov model is used to predict the number of tourist attractions, and the scoring mechanism of tourist attractions is constructed based on multiple perspectives. The constraints are the distance estimation, number prediction, scoring and user preference identification of tourist attractions. The improved particle swarm optimisation algorithm is used to find the optimal solution of recommendation and recommend the tourist path for users. The experimental results show that the average absolute error value of the proposed intelligent travel path recommendation method is 8.13, the inverse relationship between the accuracy and recall rate is clear, and it has better recommendation effect.

Online publication date: Mon, 03-Aug-2020

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