# Personalized Tour Itinerary Recommendation Algorithm Based on Tourist Comprehensive Satisfaction
## 4 Method
### Time Planning Algorithm Based on Tour Itinerary Planning Satisfaction
Figure 5: The change trend of the number of tour itinerary planning requirements, new tour itinerary planning instances, and duplicate tour itinerary planning instances with the number of iterations \(maxLoop\) (Figure (a), (b), and (c) correspond to cases 1, 2, and 3, respectively). According to Figure 6, as the heuristic weight \(\alpha_{H}\) increases, the number of demands for itinerary planning and the quantity of duplicate itinerary planning instances gradually increase, while the number of new itinerary planning instances first increases and then decreases. The increase in these event quantities is due to the inclusion of nodes with higher time efficiency in the heuristic guidance, reducing the time cost of itinerary distance and allowing more scenic spots to be included in the itinerary within a limited time. The decrease in the number of new itinerary planning instances is caused by the increase in the heuristic weight \(\alpha_{H}\), which leads to an excessive reliance on factors contained in the heuristic, thereby narrowing the solution space and generating numerous duplicate itinerary planning instances. Since our itinerary recommendation aims to find the optimal solution among all results, an excessive reliance on the heuristic may reduce our exploration space, potentially missing better solutions. Therefore, we set the heuristic weight to the value that maximizes the number of new itinerary planning instances. Considering the situations of the three cases mentioned above, we set the heuristic weight \(\alpha_{H}\) to 0.6. It can be observed that as the planned tour duration increases across the scenarios, the intersection of the number of new itinerary planning instances and the number of duplicate itinerary planning instances occurs earlier in both Figures 5 and 6. This is because, with a constant number of candidate nodes, for cases where more nodes can be added, the itinerary result set will contain a larger proportion of common nodes. This leads to an increase in the proportion of duplicate itinerary planning instances. ### Tour Itinerary Planning Algorithm Heuristic Effectiveness Analysis

Because time utilization and itinerary feasibility are not directly related to known factors, it is difficult to obtain heuristic information directly tailored to the objective function through preprocessing. We adopted the heuristic method from the original LKH algorithm, which is designed to optimize route costs. Taking the nodes in the recommended itinerary and corresponding conditions from Case 3 in Table 1 as an example, we conducted experiments to analyze the effectiveness of the route cost heuristic for our objective optimization. Firstly, we compared random sequences with sequences generated using the LKH_TP algorithm and evaluated whether these sequences could form an eligible itinerary using a time planning algorithm. Figure 7 illustrates the distribution of route costs for random sequences, eligible solutions in random sequences, sequences generated using the heuristic, and eligible solutions in sequences generated using the heuristic. Table 3 indicates the number of occurrences for each event. The results show that the validity rate of random sequences is 0.18%, while the validity rate of sequences generated using the heuristic is 25.74%. Figure 6: The change trend of the number of tour itinerary planning requirements, new tour itinerary planning instances, and duplicate tour itinerary planning instances with the heuristic weight \(\alpha_{H}\) (Figure **(a)**, **(b)**, and **(c)** correspond to cases 1, 2, and 3, respectively). We can observe that the route heuristic aids in exploring sequences with lower route costs, and eligible solutions tend to have sequences with lower route costs. The use of heuristics significantly increases the qualification rate of experimental sequences, facilitating the discovery of eligible solutions and itinerary optimization. Furthermore, we analyzed the impact of route costs on tour itinerary planning satisfaction.