# Personalized Tour Itinerary Recommendation Algorithm Based on Tourist Comprehensive Satisfaction
###### Abstract  
Personalized travel itinerary recommendation algorithms are the focus of research in smart tourism and tourism GIS. Aiming to address issues present in travel itinerary recommendations for the increasingly popular \"self-drive tour\" mode, this study proposes an algorithm based on comprehensive tourist satisfaction to mitigate problems such as the neglect of important relevant factors and low degree of personalization. First, we construct a model of tourist satisfaction for travel itineraries by comprehensively considering factors including time utilization, the attractiveness of attractions, itinerary feasibility, and the diversity of attraction types. Unlike previous studies, we consider dining and accommodation time during the itinerary, the physical condition of tourists, and the diversity of attraction types, and establish penalty functions to flexibly constrain deviations from the expected conditions in itinerary planning. Then, with the optimization of comprehensive tourist satisfaction as the objective, we design a new algorithm to address the itinerary recommendation problem, supporting tourists in selecting must-visit attractions, restaurants, and hotels, as well as personalized preferences such as the sightseeing sequence. The experimental results demonstrate that our proposed algorithm outperforms two baseline algorithms, providing higher comprehensive tourist satisfaction while also exhibiting greater feasibility in itinerary planning. The proposed algorithm effectively addresses the issue of personalized travel itinerary recommendation, presenting an efficient, feasible, and practical solution. t +
Footnote †: journal: Applied Sciences  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Personalized Tour  
**University of California, Los Angeles,  
scheduling of dining and accommodation time. This oversight could result in deviations from travelers' original plans during their journeys. Additionally, the recommended results often lack consideration for the diversity of scenic categories, which may lead to monotony and cause aesthetic fatigue during the journey. Furthermore, the majority of studies have not addressed the physical condition of travelers during their itinerary, potentially leading to fatigue during the journey. At the algorithmic level, the related studies have not elucidated how to handle multiple time windows for nodes, which is crucial for multi-day itinerary planning and the scheduling of specific visit times for tourists. Moreover, personalization is generally weak, as most studies do not support the inclusion of selected tourist attractions, restaurants, and hotels or specific node visit sequences in recommendation requirements. Additionally, in most studies, recommendations for scenic spots primarily consider the intrinsic factors of the spots and user interests but fail to comprehensively assess their value within the broader context of user-defined factors and other potential scenic spots. This lack of comprehensive consideration hinders the generation of optimal itineraries. To illustrate these issues, let us assume that [PERSON] plans a three-day self-drive tour. He has arranged the departure and return times, as well as the starting and ending points of the trip. Additionally, he wishes to maintain a regular routine during the trip, including consistent meal and rest times. He plans to have lunch at Restaurant A on the second afternoon and has booked Hotel A for the first night. He hopes to visit Scenic Spot A in the morning and Scenic Spot C after that, while also visiting other attractions to meet the planned travel time. He hopes this trip will include some local historical and cultural attractions as well as rural scenic spots. Furthermore, he wants to avoid the fatigue caused by visiting multiple mountain attractions in one day and stay within a certain budget for the entrance fees of the attractions.