Multicriteria Recommendation Method of Tourist Routes Based on Tourist Clustering

Abstract

A multicriteria recommendation algorithm for tourist routes under the multidimensional expanded spatial grid structure model based on fuzzy C-means clustering of tourist preferences is proposed. The purpose of the proposed structure is to improve the multicriteria intelligent recommendation ability of tourist routes under the multidimensional expanded spatial grid structure model of popular tourist core circle. The multidimensional enlarged spatial grid structure model of the well-known tourist core circle is used to develop the tourist correlation model of travel routes under the restricted sample training. Under the popular traveler core circle’s multidimensional enlarged spatial grid structure framework, the mixed kernel use and global kernel use are created to extract the correlation characteristics of tourist route recommendation. Under the multidimensional enlarged spatial grid structure model of the well-known tourist core circle, the adaptive learning of tourist route selection is carried out using the hybrid particle swarm algorithm. To manage the convergence of the suggestion process, maps of logistical chaos are employed, utilizing culture’s universalism and ergodicity resources and examining the aesthetic resources along tourism routes. The simulation results demonstrate that the multidimensional extended spatial grid structure model’s tourism route information recommendation accuracy is decent, and a solid swarm intelligence junction is observed in this analysis, preventing tourism route recommendations from settling for the local optimal solution and enhancing their intelligence and global stability.