Personalized tourism recommendation model based on temporal multilayer sequential neural network.

ABSTRACT: Personalized tourism has recently become an increasingly popular mode of travel. Effective personalized route recommendations must consider numerous complex factors, including the vast historical trajectory of tourism, individual traveler preferences, and real-time environmental conditions. However, the large temporal and spatial spans of trajectory data pose significant challenges to achieving high relevance and accuracy in personalized route recommendation systems. This study addresses these challenges by proposing a personalized tourism route recommendation model, the Temporal Multilayer Sequential Neural Network (TMS-Net). The fixed-length trajectory segmentation method designed in TMS-Net can adaptively adjust the segmentation length of tourist trajectories, effectively addressing the

SUBMITTER: Xiao X 

PROVIDER: S-EPMC11696103 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature