POI Recommendation Scheme Based on User Activity Patterns and Category Similarity

The utilization of location-based social networks to provide point-of-interest (POI) recommendation services has been the subject of extensive research in recent years. Various factors that can enhance the precision of POI recommendations were examined in previous studies. However, the factors of a user, including the location and time, were not considered. In this paper, we proposed a POI recommendation scheme in which user activity patterns and the similarity of categories are considered. The proposed scheme is used to organize users based on the activity level and to take into account the characteristics of both the user and location. Furthermore, it provides personalized recommendations by considering the category similarity, time, and location data that were collected from users. We evaluated the performance of the proposed scheme and compared it with that of a currently used scheme. The proposed scheme exhibits precision that is approximately 16% greater than that of the existing scheme.