# Personalized Tour Itinerary Recommendation Algorithm Based on Tourist Comprehensive Satisfaction
## 4 Method
### Time Planning Algorithm Based on Tour Itinerary Planning Satisfaction

Table 6: Analysis of penalty sources in itinerary planning results. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{**Algorithm**} & \
\begin{tabular}{c} _TravelT_ \\ **(min)** \\ \end{tabular} & _Timeout_ & **UnableVisit** &
\begin{tabular}{c} _AVisDur_ \\ **(min)** \\ \end{tabular} & _AEV DurRatio_ & _Penalty_ & _TPSS_ \\ \hline \multirow{3}{*}{1} & LKH & 46 & 0 & 0 & 420 & 100.00\% & 9 & 0.727 \\  & LKH\_TW & 66 & 0 & 0 & 420(420) & 100.00\% & 13(0) & 0.723(0.737) \\  & LKH\_TP & 46 & 0 & 0 & 420 & 100.00\% & 9 & 0.727 \\ \hline \multirow{3}{*}{2} & LKH & 238 & 0 & 1 & 1246 & 85.58\% & 88 + M & 0 \\  & LKH\_TW & 260 & 1 & 0 & 1265(1412) & 86.88\% & 344 + M \({}^{1}\)(29) & 0(0.821) \\  & LKH\_TP & 264 & 0 & 0 & 1424 & 97.80\% & 68 & 0.817 \\ \hline \multirow{3}{*}{3} & LKH & 426 & 0 & 1 & 2128 & 83.98\% & 231 + 2M & 0 \\  & LKH\_TW & 465 & 0 & 1 & 2062(2504) & 81.37\% & 268 + & \\ \cline{1-1}  & LKH\_TP & 473 & 0 & 0 & 2489 & 98.22\% & 189 & 0.848 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The evaluation metrics results of tour itinerary planning experiments. time arrangements. It is evident that dining and accommodation time have a significant impact on itinerary planning. Meanwhile, our algorithm yields optimal results in these four indicators, with an average \(AEVDurRatio\) reaching 98.67%, which is 9.82% higher than that of the LKH algorithm and 10.34% higher than that of the LKH-TW algorithm. Through the above analysis, it can be observed that considering the arrangement of dining and accommodation time is of significant importance for detailed itinerary planning under the self-drive tour mode. Additionally, the results of the itinerary planning algorithm proposed in this paper approach the expected conditions across various indicators, ensuring a relatively complete expected visitation time and regular meal and rest schedules. The provided itinerary planning results are highly feasible. ### Analysis of Tour Itinerary Recommendation Results

We conducted an analysis of the tour itinerary recommendation results through experiments. To evaluate the optimization performance of the algorithm under the same rules, we developed two algorithms as baseline algorithms. These algorithms integrate our time planning algorithm with advanced related algorithms and utilize the same objective function as our algorithm. The two baseline algorithms are as follows:

1. GreedyLKH_TR: This algorithm combines our tour itinerary planning approach with the sequential attraction insertion method proposed in [9; 10]. It begins with the basic node combination and attempts to add each candidate attraction to the current node combination individually. Then, it applies the LKH_TP algorithm to plan the itinerary for each combination and selects the node that maximizes the objective function for addition. This process continues until no more attractions can be inserted. We refer to this algorithm as the GreedyLKH_TR algorithm. 2. MCTS_TR: This algorithm combines our time planning approach with a variant of the MCTS algorithm proposed in [4; 31]. Its main process is similar to ours, but after selecting a node each time, it directly inserts it at the end of the current itinerary to form the itinerary sequence, without going through itinerary planning. The selection function used additionally incorporates a heuristic for the route cost to the current node on top of our algorithm and adjusts the total number of times all nodes in the exploration term of the selection function have been chosen to the number of times the current node has been selected. The feedback-related function remains the same as in our algorithm. We set the number of iterations to 10,000. On top of the three cases in Table 1, the experimental cases added the user's interest label as IS = {C1: Historical and Cultural}, while the remaining settings still used default parameters. We employed the following six evaluation indicators to measure the provided itinerary solutions:

1.