# Personalized Tour Itinerary Recommendation Algorithm Based on Tourist Comprehensive Satisfaction
## 3 Problem Definition and Modeling
### Problem Definition  
We define the tourism itinerary recommendation problem as follows: considering practical circumstances and expected demands in travel arrangements, we provide itinerary planning containing recommended attractions. The primary objective is to maximize the popularity and user interest in attractions, as well as the duration of attraction visits, while minimizing the deviation between estimated actual circumstances and expected conditions. In the next section, we formalize these factors and integrate them into a comprehensive satisfaction score. Additionally, the problem definition includes supporting nodes with multiple time windows, as well as various personalized settings. This study focuses on formulating comprehensive rules by considering multiple factors and designing optimization methods to solve the complex mathematical problems proposed. It aims to provide itinerary planning references for tourists by solving the recommended scenic spots, the visiting order of nodes, and the time planning of trips, combined with path planning data between two points. The proposed algorithm can be applied independently or integrated as a complementary rule component with data-driven methods to enhance their flexibility and applicability. The system framework illustrated in Figure 1 elucidates the service implementation approach of the algorithm. Tourists set their preferences and requirements through an interactive interface based on provided tourism information and their own needs. The system middleware converts the data into a format supported by the algorithm, and the resulting itinerary recommendations are presented to tourists via the algorithm. Figure 2 demonstrates the style of our results, where route information is derived from the sequence of node visits and corresponding route planning strategies, and time planning includes key time points during the journey and arrangements for dining and accommodation, providing tourists with time references closest to their expected conditions. Tourists can modify their requirements based on the results, and the system continuously optimizes the results based on user feedback. Figure 1: Proposed system framework. The system database stores attraction attribute data extracted from tourism websites and map services, as well as travel time data between attractions under different time and route planning strategies, providing data support for tourism information display and algorithm operation. The system middleware integrates user settings with database data, obtaining travel time data for nodes outside the database by calling path planning services. It also converts user settings and database data into the format required by the algorithm through basic data calculations, including node time window calculations, user interest level calculations for nodes, and input data validation. In addition to essential planned travel time and journey start and end points, the designed system offers a range of optional settings. Tourists can make selections based on their needs and preferences, as detailed in Figure 2. These personalized settings are achieved in two aspects. On the one hand, they are implemented through corresponding processing methods designed within the algorithm, such as selected attractions, hotels, and restaurants; the consideration of diversity in attraction types; specified node visit sequences; dining within attractions; and the exclusion of candidate attractions. On the other hand, these personalized settings benefit from the introduction of a wide range of factors in the problem; modifications to default parameters enable the fulfillment of corresponding personalized requirements, such as path planning strategies, travel modes, dining and sleeping times, expected stay and visit times for nodes (achieved by modifying node time windows), types of attractions of interest, budget constraints for attraction visits, regional restrictions for recommended attractions, and excluded candidate attractions.