# A Tour Recommendation System Considering Implicit and Dynamic Information
## 1 Introduction  
Tourism has emerged as a dominant sector within the global service industry, with the World Tourism Organization (UNWTO) reporting a 5% increase in international tourist arrivals, reaching 1.087 billion in 2019. Despite a temporary decline attributed to the COVID-19 pandemic, the UNWTO forecasts a rebound to 2019's pre-pandemic levels by 2023 [1]. In addition, due to the rapid development of social media, more people like self-guided tours than package itineraries planned by travel agencies. However, making an itinerary plan for most tourists is still tricky and time-consuming because they are unfamiliar with the Point-of-Interest (POI) they plan to visit. Although some Internet search engines provide helpful suggestions about popular POIs, most return scattered attractions that are difficult to link to form a complete itinerary. Therefore, developing tour recommendation systems that provide practical and quick itinerary suggestions for tourists becomes an important research topic. To fulfill this gap, much research has established different tour recommendation systems. Typically, researchers built their tour recommendation systems based on optimizationapproaches [2; 3; 4; 5] or historical travel information of users [6; 7; 8; 9; 10]. Although previous research did provide valuable solutions to the tour recommendation, some problems have been revealed. First, most previous studies used explicit information on POIs, such as category or area, to identify whether two POIs are similar or not. For example, Alcatraz Island in San Francisco is usually considered a \"historic site\". However, the site also contains implicit concepts of \"national park\" and \"island\". Although explicit information is easy to operate, many potential features in POIs might be missed. Second, to reduce the computational complexity and increase the recommendation accuracy, one common approach is to cluster users into sub-groups and then make a suggestion based on the behavior in the sub-group similar to a target user. When clustering, most previous studies only consider how many POIs are visited in common instead of their visiting order. For example, assume the visiting sequence of user A is \(<\mathit{poi}_{1},\mathit{poi}_{2},\mathit{poi}_{3}>\) and user B is \(<\mathit{poi}_{3},\mathit{poi}_{2},\mathit{poi}_{1}>\). Users A and B will be recognized as similar if no concept of visiting order is involved. However, the visiting sequences of the two users are very different. Failing to consider the visiting sequence might dramatically affect recommendation accuracy. Third, when planning the itinerary, knowing how long users might want to stay on POIs is necessary. Most previous studies take the average staying time of all users in a POI when evaluating the total travel time. However, the staying time might vary for different persons in different time slots. For example, older people stay longer than young people in a park. In addition, the time spent in the evening is usually longer than that spent in the evening for Broadway shows. If the staying time for different users, time slots, and POIs can be derived dynamically, the tour recommendation should be more precise. To solve the above problems, a novel tour recommendation system is proposed. The proposed tour recommendation system focuses primarily on optimizing user satisfaction by maximizing visit potential while also improving the accuracy of recommendations. The system's design emphasizes user preferences, dynamic factors like staying time, and visiting sequences to ensure that the recommended itinerary is both practical and personalized. First, instead of adopting explicit information to represent each POI, this research uses the implicit feature extracted from the textual description of the POI as a POI representation. A textual description for a POI contains rich information about the POI's type, facilities, or activities. The implicit feature of each POI can be obtained through appropriate text-preprocessing and feature-reduction techniques.