# Exploring Prior Knowledge from Human Mobility Patterns for POI Recommendation
## 1 Introduction  
Undoubtedly, smart tourism is an important component of smart cities. As tourist destinations continue to develop and change, people's travel choices are becoming more diverse and complex. Smart tourism aims to intelligently connect and manage cities and tourism resources using technologies such as the Internet of Things (IoT), big data, and artificial intelligence in order to provide personalized tourism services to travelers [1]. In the context of deep learning technology, Point of Interest (POI) recommendation plays a crucial role in smart tourism. It can extract preference information from users' historical travel data and provide them with greater convenience and satisfaction in their travel experiences. POI recommendation can also alleviate the current situation of over-tourism [2], reducing the burden and environmental pressure of tourism destinations. On the other hand, the Internet of Things (IoT), as a network infrastructure, collects various needed information in real-time through various technologies, realizes the ubiquitous connection between things and other things and between things and people, andachieves intelligent perception of objects and processes [3; 4]. Through sensors and devices in the Internet of Things (IoT), environmental information, foot traffic, traffic conditions, and other data surrounding Points of Interest (POIs) can be collected and monitored. These dynamic and heterogeneous data can be used for real-time monitoring, analysis, and prediction of POIs, thereby providing more accurate recommendations and services. Due to the widespread use of mobile positioning devices (smartphones) and the rapid development of Location-Based Social Networks (LBSNs) [5], users can share location data by checking in at POIs on social networks [6]. Through LBSNs, users can record and share their activities and experiences at different POIs, generating a large number of POI-related data [7]. These POI-related data include users' interests and behavioral information at specific locations, which can reflect the preferences of user groups. These data can be used to discover human mobility patterns and provide important reference for POI recommendation systems. POI recommendation is an important way to assist users in exploring the surrounding environment to improve user experience. The POI recommendation algorithm provides users with personalized recommendation services by learning user historical data. At present, the mainstream POI recommendation system focuses on learning users' preferences from historical social network data to make recommendations [8]. However, the results obtained in this way may be counter-intuitive, such as recommending a downtown cafe on a non-weeknight. Therefore, POI recommendation should combine prior knowledge of human mobility patterns [9]. In order to efficiently explore prior knowledge from human mobility patterns, various data in cities (traffic trajectory data, map data, location data, social media data, consumption data, etc.) should be effectively utilized. At present, understanding how to effectively represent the relationship between multi-source heterogeneous data in cities and how to extract and analyze the features of multi-source heterogeneous data relationships are still important and urgent problems to be solved in the research on human mobility patterns. In addition, studies purely aimed at extracting human mobility patterns are rare. Human mobility patterns are usually an intermediate result of completing a specific downstream task. Understanding how to select downstream tasks and verify the quality of the obtained human mobility patterns is also a problem investigated in this paper. To address these problems, we propose a POI recommendation method based on prior knowledge of human mobility patterns. This method makes full use of the prior knowledge of human mobility patterns to solve the difficult problem of choice caused by a large amount of promotional information flooded in social networks.