A New Point-of-Interest Classification Model with an Extreme Learning Machine

Abstract

With the increasing popularity of location-based social networks (LBSNs), an increasing number of people are sharing their locations with friends through check-in activities. Point-of-interest (POI) recommendation, in which new places are suggested to users, is one of the most important tasks in LBSNs. However, after recommendation, it is also of interest to consider whether a user will frequently visit a recommended POI, which may have significant implications regarding the user’s daily mobility behavior or personal preferences. Therefore, in this paper, we propose a new POI classification problem in which the POIs recommended to a user are divided into four classes according to the user’s predicted future check-in frequency: daily check-in POIs, weekly check-in POIs, monthly check-in POIs, and yearly check-in POIs. To solve this POI classification problem, we also propose a new POI classification model called POIC-ELM. In the POIC-ELM model, we first extract nine features related to three factors: each POI itself, the user’s personality, and the user’s social relationships. Then, we use these features to train a POI classifier based on an extreme learning machine (ELM), which is one of the most popular types of classifiers among state-of-the-art classification techniques. A series of experiments show that the effectiveness and efficiency of POIC-ELM are superior to those of other methods. The POIC-ELM model is a valid method for solving the POI classification problem.