Cognitive Computation

, Volume 10, Issue 6, pp 951–964 | Cite as

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

  • Zhen Zhang
  • Xiangguo ZhaoEmail author
  • Guoren Wang
  • Xin Bi


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.


Location-based social networks POI recommendation POI classification Extreme learning machine 


Funding Information

This research was partially supported by the National Natural Science Foundation of China under Grant Nos. 61672145, 61572121, 61602323, 61702086, and U1401256.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflicts of interest.

Human and animal rights and informed consent

This article does not report any studies involving human participants and/or animals by any of the authors. Informed consent was obtained from all individual participants.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Computer Science and EngineeringNortheastern UniversityShenyangChina
  2. 2.Sino-Dutch Biomedical and Information EngineeringNortheastern UniversityShenyangChina

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