Deep Route Recommendation (DRR): A Context-Aware Attention-Based Deep Learning Framework for Personalized Travel Route Planning

Abstract

In the dynamic discipline of clever tourism, personalized journey pointers have grow to be critical for catering to the numerous options of modern travelers. This research introduces DeepRouteRecommendation (DRR), an innovative deep learning-based framework designed to craft context-conscious and consumer-centered tour itineraries. DRR stands out by using incorporating a wide array of user statistics, along with demographic facts, beyond tour behaviors, user preferences, and subtle comments, to create information of user decision-making strategies. The framework employs a complex multi-layer neural network architecture, complemented by sequence modeling strategies, to maintain the spatial and temporal coherence of the factors of interest (POIs). To strengthen methodological clarity, the abstract now explicitly specifies that DRR uses an LSTM-based sequence encoder combined with an attention mechanism to align user intent with POI characteristics. Additionally, a constraint-conscious optimization module ensures that the generated itineraries are realistic, taking into consideration factors such as budget constraints, time availability, and the accessibility of POIs. The evaluation was performed on a clearly defined hybrid dataset comprising real-world POI data (TripAdvisor + OpenStreetMap) integrated with synthetically generated user profiles to ensure diverse behavioral patterns. The outcomes verified that DRR drastically outperformed conventional recommendation structures, as well as Collaborative Filtering (CF), Content-Based Filtering (CBF), and Reinforcement Learning-based Route (RL-Route) techniques. Specifically, DRR outperformed the strongest baseline (RL-Route) by 12.3% in Recall, 11.7% in Precision, and 10.8% in Diversity, achieving a Recall of 82.4%, a Precision of 79.6%, and a Diversity score of 81.2%.