# POI Recommendation Scheme Based on User Activity Patterns and Category Similarity
## 1 Introduction  
Location-based social networks (LBSNs) have been the subject of extensive research in recent years [1; 2]. People can use LBSNs to communicate and interact with each other by sharing their current location, frequently visited locations, events, and activities. Users can share their locations and receive information regarding nearby stores, restaurants, and attractions through LBSNs. These details encompass reviews, ratings, and information regarding nearby locations. Additionally, LBSNs facilitate interactions associated with events and gatherings that take place in close proximity to users. Recommendation services, in which the LBSNs based on these characteristics are employed, offer personalized recommendations for locations or events to users, thereby increasing user engagement and promoting social network activities. This helps users explore new locations or connect with others. Customized recommendations in which each user's unique preferences and interests are considered are becoming essential in recommendation services that employ LBSNs. A recommendation system for points of interest (POIs) based on LBSNs recommends POIs that are appropriate for each user as per the information the user has entered. The fundamental factors to consider are time and spatial constraints and geographical characteristics. Information regarding a user's current location and the time period they are visiting is extracted and utilized. However, existing schemes only use time and location to exclude candidate places from the recommendation list. For example, they exclude places that are not open at the current time or those that are too far away. Existing schemes do not perform analysis based on time periods and location to provide personalized recommendations. Research has been conducted to evaluate various factors that can enhance the precision of POI recommendations. Various situational factors that influence users have been considered, including the utilization of a Voronoi diagram to create a virtual tracking pattern [1], prediction of the user's preference in the results through the retention rate [2], and recommendations of appropriate locations for each user group by segmenting users [3]. A representative study describes a methodology in which an algorithm with high adaptability to the user group is employed by segmenting users [4]. However, the predictability and accuracy of the algorithm are significantly lower than those of the collaborative filtering methodology when assessed using actual data. Recent studies have been conducted on POI recommendations using machine learning [5; 6]. These studies introduce recommendation models that achieve high accuracy using deep learning. However, there are limitations; most users do not have enough data for personalized recommendations, and machine learning consumes a significant amount of computing resources. In this paper, we propose a POI recommendation scheme in which user activity patterns and the similarity of categories are considered. Figure 1 shows the relationships between the factors considered by the proposed scheme and those considered by existing schemes. The proposed scheme organizes users based on the activity level and considers the characteristics of both the user and the location. Furthermore, it provides more personalized recommendations by taking into account the category similarity, time, and location data that are collected from users. The proposed scheme provides more accurate personalized recommendations by considering user activity and category similarity. We evaluate the performance of the proposed scheme and compare it with that of a currently used scheme to demonstrate effectiveness. The remainder of this paper is structured as follows. Section 2 describes the limitations and characteristics of previous studies on POI recommendations. Section 3 introduces the proposed POI recommendation scheme. Section 4 presents a comparison between the proposed scheme and an existing scheme.