# Personalized Tour Itinerary Recommendation Algorithm Based on Tourist Comprehensive Satisfaction
###### Abstract
This example illustrates a complex itinerary planning demand, highlighting the limitations of traditional recommendation systems in handling complex personalized needs. This paper addresses the aforementioned issues by proposing a personalized tour itinerary recommendation algorithm based on tourists' comprehensive satisfaction, specifically tailored for the self-drive tourism mode. The algorithm is characterized by a comprehensive consideration of factors and rich personalization settings, while also balancing efficiency and optimization performance. The main contributions of this study can be summarized as follows. Firstly, we constructed a comprehensive satisfaction score for the itinerary and formulated a problem model with this indicator as the objective function. We introduced a wide range of factors into the problem, including the type, grade, popularity, opening hours, recommended visiting time, accommodation and dining availability, visitor interest types, meal and rest time preferences, physical limitations, budget constraints, planned travel time, planned starting and ending points, other personalized settings, and the time cost of the itinerary. Based on these factors, we constructed indicators for the time utilization rate, attractiveness, feasibility, and diversity of attraction types to form the comprehensive satisfaction score for tourists. Additionally, in our model, some constraint conditions are flexible, and we used penalty functions to evaluate and constrain deviations between the itinerary plan and the expected conditions, making the itinerary planning more flexible and personalized. Secondly, we proposed a novel algorithm based on a variant of the Monte Carlo Tree Search (MCTS) algorithm and the Lin-Kernighan Heuristic (LKH) algorithm to solve the proposed problem. This algorithm fully utilizes the search capabilities of the MCTS algorithm in large search spaces and the optimization capabilities of the LKH algorithm in the Traveling Salesman Problem, efficiently and effectively addressing the itinerary recommendation problem by decomposing it into local subproblems. In the algorithm, we designed a time planning algorithm based on the minimum penalty principle and redesigned some functions in the MCTS and LKH algorithms. Furthermore, we improved the algorithm's efficiency through the use of hash tables. The proposed algorithm supports nodes with multiple time windows, allowing tourists to select must-visit attractions, restaurants, and hotels and choose specific node visitation sequences and other personalized preferences. The experimental results using real data demonstrate that the proposed algorithm can provide itinerary recommendations with higher comprehensive satisfaction and greater feasibility in itinerary planning. Thirdly, we proposed a system framework outlining the service approach of the algorithm and the supported personalized settings. In the designed system, tourists can input their requirements and preferences through basic settings and extensive optional settings and can then obtain detailed references for their travel itineraries in terms of both time and route. The rest of this paper is organized as follows. Section 2 reviews the related work. Section 3 introduces the problem definition, proposed system framework, and constructed mathematical model. Section 4 presents the proposed algorithm. Section 5 describes the conducted experiments and analyzes the results. Finally, Section 6 concludes the work and proposes future directions for improvement.