## 1 Introduction
To query relatively simple items, such as restaurants or shops, content-based and collaborative filtering-based recommendation strategies are usually applied. Content-based strategies recommend services of tourism based on the similarity of user preferences and the descriptive information of services [10]. Collaborative filtering recommendation strategies are based on the opinions of users who share similar interests or locations [11; 12; 13]. In addition, knowledge-based and hybrid recommendation strategies are utilized to query more complex items, such as travel routes and plans [14; 15]. Knowledge-based recommendation strategies offer items to users based on the knowledge about the relationship between users' requirement and a possible recommendation. The hybrid recommendation approach combining two or more recommendation strategies into one hybrid strategy was proposed in [16]. Moreover, a system based on the technologies of big data and time series analysis for smart tourism is designed in [17]. In recent years, social network analysis recommendation strategies based on spatial or attribute information obtained with the social networking tools have been proposed due to the dramatic growth of computer sciences and technologies [18; 19]. Additionally, because mobile service information provides extra references in tourists' behavior, many recommendation strategies based on the new technologies, such as RFID and augmented reality (AR), are widely used in tourism and leisure industries [20; 21]. In addition, the novel recommendation system based on artificial intelligence to provide the personalized favorite music is introduced in [22]. This study designed and implemented a theme park tourist service (TPTS) system with the proposed personalized recommendation algorithm to enhance the visitor experiences. The TPTS system comprises four subsystems: the mobile app subsystem, the ticket-scanning subsystem, the detecting/counting subsystem, and the central subsystem. The mobile app subsystem is a tool for tourists to take advantage of diverse system services such as query of park information and reservation of preferred attractions. The ticket-scanning subsystem scans the tourist's reservation (booking) ticket and sends the ticket information to the central subsystem for further verification. The detecting/counting subsystem aims to detect and compute the queue length and accumulated visit count of each attraction. The central subsystem performs necessary computing and database management of the proposed system. The proposed TPTS system introduces an innovative function called the personalized dynamic scheduling to offer tourists the customized best plans mainly according to the location of tourists, the queue length, the capacity, and the duration of an attraction. A tourist can use the mobile app to establish his/her own favorite attraction list, choose his/her own preferred attraction priority, and obtain the personalized recommended attraction to visit next. In addition, we design location-based dynamic map and attraction reservation functions into the TPTS system. The main contributions of this study are summarized as follows. The TPTS system eliminates choice overload because it leaves only three commonly considered strategies (i.e., closest attraction first, shortest wait-time attraction first, and hottest attraction first) for tourists to decide on their own. In addition, the TPTS system removes the tourist's pressure caused by searching for only one preferred attraction from lengthy suggested attraction list because it offers only one recommended attraction at a time, and the recommendation is solely derived from the tourist's favorite attraction list. In addition, the recommended attraction is always the timely best attraction according to the current conditions of the attractions in the theme park, not an out-of-date or out-of-time result in a pre-arranged recommended list.