Volume 16 Number 2 (Mar. 2021)
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JSW 2021 Vol.16(2): 46-54 ISSN: 1796-217X
doi: 10.17706/jsw.16.2.46-54

A Potential Friend Recommendation Algorithm for Obtaining Spatial Information

Hang Zhang, Zhongliang Cai*
School of Resource and Environmental and Sciences, Wuhan University, China

Abstract—With the rapid development of social network, friend recommendation algorithm has become an important component of social application. Location-based social network (LBSN) enables users to record and share their locations anytime and anywhere, which is a high quality information source. In order to meet people's demand of expanding social circle and obtaining diversified spatial information when making friends, this paper proposes a potential friend recommendation algorithm based on the similarity of user's check-in behavior and spatial information acquisition level in the real world. Firstly, we employ kernel density estimation and time entropy to solve the problems of data sparsity and low concentration, then employ cosine distance to measure the check-in behavior similarity. Secondly, we analyze users’ spatial distribution of check-in location and cognitive differences on spatial information. Finally, the method mentioned above is tested with dataset called Foursquare. The results of the experiment show that the proposed method has competitive performance.

Index Terms—Recommendation algorithm, LBSN (location-based mobile social network), spatial information acquisition, time entropy.


Cite: Hang Zhang, Zhongliang Cai, "A Potential Friend Recommendation Algorithm for Obtaining Spatial Information," Journal of Software vol. 16, no. 2, pp. 46-54, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0)


General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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