# Personalized Tour Itinerary Recommendation Algorithm Based on Tourist Comprehensive Satisfaction
## 4 Method
### Time Planning Algorithm Based on Tour Itinerary Planning Satisfaction
Section 5.5 explores the importance of considering the dining and accommodation times in itinerary planning by comparing them with a baseline algorithm that does not consider the dining and accommodation times. Finally, Section 5.6 evaluates the optimization performance of the proposed algorithm by comparing it with other algorithms. The algorithm proposed in this paper and all baseline algorithms are developed using the C language. All experiments were conducted on a computer with the following specifications: CPU: Intel Core i7-12700H 2.30 GHz, RAM: 16 GB. ### Experimental Data

The experiment took the entire area of Hanzhong City as the research area, with a total area of approximately 27,200 square kilometers. Fifty key scenic spots in Hanzhong City were selected for the study. The distribution of the study area and selected scenic spots are shown in Figure 4. One hotel was selected to simulate the user's chosen starting point and destination. The travel time data between scenic spots was obtained from Tencent Maps' route planning service, which provides estimated travel times based on both distance and traffic conditions derived from big data. Information such as the popularity, opening hours, ticket prices, and categories of the scenic spots was sourced from Amap and Ctrip. The evaluation of the scenic spots is based on ratings from two websites and recent visitation numbers, comprising three factors in total. Among them, the ratings of the scenic spots are generated from user data. The recommended duration for visiting each scenic spot was sourced from Mafengwo and local tourism information websites. ### Parameter Debugging of Tour Itinerary Recommendation Algorithm

We conducted experiments to determine how to set the iteration count (\(maxLoop\)) and heuristic weight (\(\alpha_{H}\)) in the MCTSLKH_TR algorithm. We performed a grid search across three cases, as shown in Table 1, to find the optimal values for these two parameters that balance efficiency and effectiveness effectively. Figure 4: Study area and distribution overview of the selected scenic spots. According to Figure 5, as the iteration count \(maxLoop\) increases, the number of new tour itinerary planning instances and number of duplicate tour itinerary planning instances both increase gradually when the number of tour itinerary planning requirements increases linearly. Moreover, the growth rate of new itinerary planning instances gradually slows down, while the growth rate of duplicate itinerary planning instances intensifies. This is because as the iteration count \(maxLoop\) increases, the solution space gradually converges, meaning that a greater proportion of itinerary planning instances has been calculated. When the growth rate of duplicate itinerary planning instances approaches the growth rate of itinerary planning requirements, it suggests that the algorithm hardly produces new solutions in the increasing iteration rounds. At this stage, the computational cost no longer offers a good balance between efficiency and value. Additionally, according to Table 2, after the growth rate of duplicate itinerary planning instances increases significantly, no better solutions are generated. Balancing algorithm efficiency and effectiveness, considering the scenarios of the three cases, we set the iteration count \(maxLoop\) to 500. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Case** & **Planned Travel Time** & **Starting Point** & **Destination** &
\begin{tabular}{c} **Remaining** \\ **Settings** \\ \end{tabular} \\ \hline
1 &
\begin{tabular}{c} One-day tour (day1 \\ 8:30–day1 21:30) \\ Three-day tour (day1 \\ 8:30–day3 21:30) \\ Five-day tour (day1 \\ 8:30–day5 21:30) \\ \end{tabular} & Hotel1 & Hotel1 & default \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental cases.