Group travel recommendation model study based on spatial and temporal integration factors

Abstract

With the increasing demand for tourism, people’s travel modes are more and more diversified, and the tourism recommendation system also arises at the historical juncture. However, the current recommendation system is only recommended for a single user and does not realize the group travel recommendation. To achieve the goal of recommending its preferred attractions for multiple users, the time decay characteristics and Pearson correlation coefficient in Newton’s cooling law are used to obtain the user similarity with spatial distance factor and temporal decay factor and to obtain the score prediction results based on spatiotemporal fusion. In addition, the trust of user communication is used to recommend, and the weights of the two scoring results are added to obtain the personalized recommendation results of member users. Finally, the study used the fusion strategy to integrate the personalized recommendation results for group preference and obtained the final group travel recommendation list. Therefore, a group travel recommendation model based on spatio-temporal integration factors was constructed. According to the experimental analysis, we can see that the average HR value of the constructed model is 0.8124, and the average NDCG value is 0.7284, which can accurately judge users’ preferences and get the most suitable group travel recommendation results, thus facilitating users to make the next plan for the tourism project.