# Personalized Tour Itinerary Recommendation Algorithm Based on Tourist Comprehensive Satisfaction
## 2 Literature Review
### Research on Itinerary Recommendation Algorithms  
In the realm of trip recommendation, a significant amount of research has been conducted focusing on orientation problems, combinatorial optimization problems, or similar paradigms. The primary aim across these studies is to maximize global rewards within user-defined time budgets [3]. [PERSON] [4], for example, leveraged a variant of the Monte Carlo Tree Search to devise an amusement park itinerary recommendation method, which takes into account factors such as the popularity of scenic spots, user interest levels, and queuing times. Similarly, [PERSON] [5] analyzed the impact of weather factors on people's travel and proposed a model of Dynamic Personalized POI Sequence Recommendation with fine-grained contexts, and solved it based on the MCTS algorithm. [PERSON] [6] formulated an integer linear programming model for trip recommendations, addressing selected scenic spots and solving it as an integer linear programming problem. Furthermore, [PERSON] [7] devised an itinerary recommendation method for family travel, utilizing an improved large-domain adaptive search algorithm to maximize overall family satisfaction under constraints. [PERSON] [8] likewise considered group-oriented tourism route recommendations, aiming to satisfy the preferences of each member in the group as much as possible. [PERSON] [9] integrated factors like scenic spot popularity, distances, and user demand constraints to derive recommended itineraries by sequentially adding optimal scenic spots. [PERSON] [10] proposed a method based on a state expansion approach to solve recommended itineraries, factoring in user interests, travel time constraints, and scenic spot time windows. In addition, [PERSON] [11] introduced a route recommendation method supplemented with random recommendations based on neural network algorithms, aiming to inject novelty into scenic spot recommendations. [PERSON] [12] devised an itinerary recommendation approach grounded in graph representation learning and reinforcement learning, taking into account scenic spot popularity and user interests. [PERSON] [13] developed an intelligent travel route planning algorithm that employs an iterative search of clustering central motivation. In this method, clustering algorithms are employed to mine tourist attraction data based on tourists' interests, thereby obtaining optimal scenic spots and recommended itineraries. Additionally, [PERSON] [14] proposeda travel recommendation algorithm based on text mining and the MP neural cell model of multiple transportation modes, achieving recommended itineraries through optimal tourism landscape matching algorithms and tourism route chain algorithms. [PERSON] (2018) presented a route planning method that comprehensively considered factors such as station distances, initial travel locations, departure times, travel durations, costs, station scores, and popularity, ultimately identifying optimal routes via genetic algorithms. [PERSON] (2018) introduced an adaptive genetic algorithm for personalized travel itinerary planning, incorporating attractions near starting and destination points as well as potential tourist preference factors into multi-objective optimization frameworks. [PERSON] (2018) proposed a multi-objective travel itinerary planning method based on ant colony algorithms, aiming to optimize travel costs and user satisfaction. A distinctive aspect of this approach involves integrating tourists' past travel records to adjust pheromones, thereby reflecting their general preferences. Meanwhile, [PERSON] (2018) addressed travel itinerary planning under time constraints and uncertain traffic conditions, using a two-stage stochastic optimization model with chance constraints and the sample average approximation (SAA) method. Additionally, [PERSON] (2018) proposed the use of multi-objective evolutionary algorithms to balance the popularity of POIs and user interest in itinerary recommendations.