# Personalized Tour Itinerary Recommendation Algorithm Based on Tourist Comprehensive Satisfaction
## 4 Method
### Time Planning Algorithm Based on Tour Itinerary Planning Satisfaction
Due to the predetermined time constraints, experimental sequences often resulted in an ineligible itinerary. To address this, we conducted the analysis without imposing a total travel time limit to ensure the integrity of the itinerary. Figure 8 illustrates the relationship between route costs and their corresponding average minimum required available time and average tour itinerary planning satisfaction. Due to the influence of time windows for tourist attractions as well as meal and accommodation times, trips with the same route costs exhibit significant differences in the minimum required available time and itinerary satisfaction. To mitigate the impact of these factors, we use averages to highlight the overall trends of the minimum required available time and itinerary satisfaction with respect to route costs. The correlation coefficients between them were found to be 0.901 and \(-\)0.914, respectively. These data were derived from the time planning results of one million random sequences without a total time limit. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Event** & **Trials** & **Eligible Solutions** & **Pass Rate** \\ \hline Random sequence & 1,000,000 & 1823 & 0.18\% \\ Heuristic sequence & 113,823 & 29,296 & 25.74\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The number of random and heuristic sequence trials and their eligible sequences. Figure 7: Route cost distribution for random and heuristic trial sequences and the eligible sequences therein. It can be predicted that under predetermined time constraints, lower route costs lead to a higher probability of forming an eligible itinerary and a reduced likelihood of time loss for visiting tourist attractions within the itinerary, owing to the competition between travel time and time spent at tourist attractions. This also increases the chances of obtaining a more satisfactory itinerary. Therefore, by emphasizing attempts with lower route costs through heuristics, we can protect the visiting time and discover a better itinerary. In conclusion, the heuristic approach to optimizing route costs is well-suited for our itinerary planning needs. ### Tour Itinerary Recommendation Algorithm Selection Function Effectiveness Analysis

We also analyzed the effectiveness of the itinerary recommendation algorithm selection function through the three cases in Table 1. Firstly, we examined the effectiveness of the added heuristic factors. Figure 9 illustrates the relationship between the heuristic factors of each candidate node and the corresponding factors in the reward for the three cases. The average correlations are 0.87, 0.76, and 0.86, respectively. It can be observed that there is a high positive correlation between the heuristic and the reward. Additionally, the heuristic of the nodes exhibits greater variability compared to the reward, especially in itineraries containing more recommended attractions. This is because the heuristic removes the influence of other nodes in the reward. The inclusion of the heuristic highlights the inherent value of candidate attractions, facilitating the selection of superior attractions among those with similar rewards, which positively contributes to discovering itineraries with higher rewards. Moreover, it can be observed that in cases with longer travel durations, attractions generally exhibit a higher time efficiency, as more nodes in the itinerary make it easier to include a larger proportion of attractions along the route. Figure 8: Relationship between route cost and average minimum required available time and average tour itinerary planning satisfaction score. Then, we further analyzed the effectiveness of the selection function by examining the average rankings of the recommended nodes in the optimal itinerary in terms of their selected number and various dimensions in the selection function (among the 50 candidate nodes). We also assessed the distribution of nodes selected in each iteration during the algorithm process.