# Personalized Tour Itinerary Recommendation Algorithm Based on Tourist Comprehensive Satisfaction
## 4 Method
### Time Planning Algorithm Based on Tour Itinerary Planning Satisfaction
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{_RecNum_} & \multicolumn{2}{c}{_AVisDur_} & \multicolumn{2}{c}{_ItinDur_} & \multirow{2}{*}{_TimePropo_} & \multirow{2}{*}{_Penalty_} & \multirow{2}{*}{_TUS_} & \multirow{2}{*}{_ISAS_} & \multirow{2}{*}{_FS_} & \multirow{2}{*}{_CS_} & \multirow{2}{*}{_RunTime_} \\  & & & **(min)** & & & & & & & **(s)** \\ \hline \multirow{3}{*}{1} & GreedyLKH\_TR & 3 & 420 & 729 & 93.46\% & 8 & 0.737 & 4.525 & 0.989 & 3.298 & 0.28 \\  & MCTS\_TR & 3 & 402 & 704 & 90.26\% & 4 & 0.705 & 4.55 & 0.994 & 3.191 & 3.77 \\  & MCTS\_LKH\_TR & 3 & 420 & 729 & 93.46\% & 8 & 0.737 & 4.525 & 0.989 & 3.298 & 0.76 \\ \hline \multirow{3}{*}{2} & GreedyLKH\_TR & 6 & 1204 & 3624 & 99.02\% & 115 & 0.704 & 4.291 & 0.968 & 2.925 & 2.27 \\  & MCTS\_TR & 8 & 1358 & 3652 & 99.78\% & 143 & 0.794 & 3.906 & 0.961 & 2.980 & 15.58 \\  & MCTS\_LKH\_TR & 10 & 1508 & 3652 & 99.78\% & 202 & 0.882 & 4.26 & 0.945 & 3.549 & 9.61 \\ \hline \multirow{3}{*}{3} & GreedyLKH\_TR & 12 & 2046 & 6503 & 99.43\% & 265 & 0.718 & 4.247 & 0.959 & 2.924 & 14.45 \\  & MCTS\_TR & 12 & 1733 & 6495 & 99.31\% & 139 & 0.608 & 4.33 & 0.979 & 2.577 & 28.03 \\ \cline{1-1}  & MCTS\_LKH\_TR & 15 & 2352 & 6532 & 99.88\% & 176 & 0.825 & 3.984 & 0.973 & 3.199 & 37.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The evaluation metrics results of tour itinerary recommendation experiments. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & _TimePropo_ & **TUS** & **ISAS** & **FS** & **CSS** & _RunTime_(s)** \\ \hline GreedyLKH\_TR & 97.30\% & 0.720 & 4.354 & 0.972 & 3.049 & 5.67 \\ MCTS\_TR & 96.45\% & 0.702 & 4.262 & 0.978 & 2.916 & 15.79 \\ MCTS\_LKH\_TR & 97.71\% & 0.815 & 4.256 & 0.969 & 3.349 & 15.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Average evaluation metric results of tour itinerary recommendation experiments. Additionally, the MCTSLKH_TR algorithm can directly control the runtime not to exceed a certain limit. The average runtime of the MCTSLKH_TR algorithm across the three cases is comparable to that of the MCTS_TR algorithm and has an advantage in small-scale cases. In conclusion, the tour itinerary recommendation algorithm proposed in this paper can achieve superior itinerary recommendations, particularly in terms of time utilization. It results in tourists being able to spend more time visiting attractions and accessing a greater number of attractions within essentially the same itinerary duration. It can be observed that the GreedyLKH_TR algorithm maintains relatively stable itinerary recommendation quality. However, the sequential insertion method is prone to becoming trapped in local optima, making it difficult to achieve better results. As the number of recommended nodes increases, the solution quality of the MCTS_TR algorithm gradually decreases. This is because with the increase in the number of recommendations, the solution space grows factorially, which cannot be compensated for by increasing the number of iterations in the MCTS_TR algorithm. Additionally, the inability to guarantee the rationality of itinerary planning reduces the reference value of itinerary rewards, and the need to select the optimal node at the current position increases the difficulty of selection. In contrast, our algorithm combines a variant of the MCTS algorithm, balancing exploration and exploitation. It ultimately selects the best option from a global perspective. Furthermore, we separate the itinerary recommendation task into node combination and itinerary planning. Through itinerary planning, we can rapidly cover a large number of solutions and obtain high-quality feedback to guide node selection. Therefore, our method can provide high-quality itinerary planning and maintain stability as the number of recommended nodes increases.