# Personalized Tour Itinerary Recommendation Algorithm Based on Tourist Comprehensive Satisfaction
## 4 Method
### Time Planning Algorithm Based on Tour Itinerary Planning Satisfaction
The reference optimal values in Table 4 are based on the theoretical optimal average ranks derived from the number of recommended nodes. It can be observed that our actual values are close to or equal to the optimal values, significantly outperforming the average rank of 25 obtained randomly. This indicates that the utilization dimensions in the selection function effectively guide the selection of nodes. Additionally, from Figure 10, it can be seen that our selection function balances exploration and exploitation, gradually converging during exploration. This not only facilitates the discovery of a high-quality itinerary but also ensures a sufficient number of attempts. Overall, our selection function demonstrates excellent effectiveness in itinerary recommendation. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{2}{c}{**Optimal Value**} & \multirow{2}{*}{_VisitCount_N_} & \multicolumn{2}{c}{_Regard_} & \multicolumn{2}{c}{_Timefficiency_} & \multirow{2}{*}{**SAS**} & \multicolumn{2}{c}{**UCB Excluding**} \\  & \multicolumn{2}{c}{**Reference**} & & \multicolumn{1}{c}{_VisitCount_N_} & \multicolumn{1}{c}{_VisitCount_N_} & & \multicolumn{1}{c}{**Exploration Part**} \\ \hline
1 & 2 & 2 & 2 & 3.667 & 5.667 & 2 \\
3 & 5 & 6.111 & 7.556 & 7.111 & 13.889 & 6.333 \\
5 & 8 & 8 & 9 & 9.125 & 15.933 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The average rankings of the recommended nodes in the optimal itinerary. Figure 10: Selection of scenic spots in each iteration (Figure (a), (b), and (c) correspond to cases 1, 2, and 3, respectively). Figure 9: The relationship between the heuristic factors of candidate nodes and their corresponding factors in the rewards: (**a**) Scenic spot attractiveness score and the average value of ISAS; (**b**) The average value of TimeEfficiency and the average value of TUS; (**c**) Heuristic in UCT and the average value of TUS * ISAS. ### Analysis of Tour Itinerary Planning Results

As a key component of the algorithm, the results of itinerary planning directly impact the itinerary arrangements in the recommendation results. We conducted an analysis of the itinerary planning results through experiments. Among the fundamental elements, travel, eating, lodging, transportation, and sightsseeing are all crucial. However, past research has often overlooked the importance of properly arranging dining and accommodation times. Therefore, we primarily focused on the significance of considering dining and lodging times in the temporal dimension. In our experiments, we used the recommended nodes and their corresponding conditions from the three cases in Table 1 for itinerary planning. We also selected the following two baseline algorithms, both based on the LKH algorithm:

1. LKH: This algorithm plans an itinerary based on minimizing route costs, which is the most basic and common method of itinerary planning. It does not consider the temporal feasibility of itinerary arrangements. 2. LKH-TW: This algorithm plans an itinerary based on the optimal \(TPSS\), employing the same objective function as presented in this paper. It considers the time windows of attractions but does not consider dining and accommodation times during the itinerary. The algorithm introduces the optimal time window selection algorithm proposed in this paper to accommodate multiple time windows of attractions in multi-day trips. Additionally, to avoid unreasonable overnight travel, any intersection between attraction time windows and resting time is removed, implying a rigid arrangement for accommodation time. This algorithm represents the current level of time constraints in itinerary planning problems in the related literature. We utilized the following seven evaluation metrics to measure the provided itinerary solutions:

1. The travel time (\(TravelT\)). 2. Whether the itinerary exceeds the planned time (\(Timeout\)) (indicated by 0 or 1). 3. Whether there are attractions that cannot be visited (\(UnableVisit\)).