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 issue of large spatiotemporal spans by integrating tourist behavior characteristics and route complexity. The self-attention mechanism incorporating relative positional information enhances the model's ability to capture the relationships between different paths within a tourism route by merging position encoding and distance information. Additionally, the multilayer Long Short-Term Memory neural network module, built through hierarchical time series modeling, deeply captures the complex temporal dependencies in travel routes, improving the relevance of the recommendation results and the ability to recognize long-duration travel behaviors. The TMS-Net model was trained on over six million trajectory data points from Chengdu City, Sichuan Province, spanning January 2016 to December 2022. The experimental results indicated that the optimal trajectory segmentation interval ranged from 0.8 to 1.2 h. The model achieved a recommendation accuracy of 88.6% and a Haversine distance error of 1.23, demonstrating its ability to accurately identify tourist points of interest and provide highly relevant recommendations. This study demonstrates the potential of TMS-Net to improve personalized tourism experiences significantly and offers new methodological insights for personalized travel recommendations.