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Road network-based region of interest mining and social relationship recommendation

  • Rong Tan
  • Yunpeng ZhangEmail author
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Abstract

Region of interest (ROI) discovery is among the most common functions in location-based social networking services (LBSNS). While former researches mainly utilize the accurate location coordinates history, the road context-based active region extraction algorithm (RAREA) proposed in this paper explores the method to extract those regions with road contexts. Furthermore, based on the active regions extracted by RAREA, the kNN consistency-based relationship recommendation algorithm (kNNC-RRA) is proposed as well. The kNNC-RRA compares the similarity degree of the active regions among the users to find the individuals with similar preferences to recommend the potential relationships. Experimental results illustrate that by analyzing the characteristics of those road contexts, ROIs are able to be discovered with high efficiency. And our work shows that both privacy protection and personalized services can be achieved in LBSNS.

Keywords

Region of interest discovery Relationship recommendation Location-based social networking services Mobile computing Location privacy protection Road network Social computing 

Notes

Acknowledgements

The authors would like to thank the reviewers for their invaluable comments and suggestions, which greatly helped to improve the presentation of this paper.

Funding

This research was sponsored by the Shanghai Natural Science Fund (Nos. 14ZR1429800, 15ZR1430000) and the Research Fund of National 12th Five-Year Education Plan (No. EIA140412).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Shanghai Business SchoolCollege of Information and Computer ScienceShanghaiPeople’s Republic of China
  2. 2.College of TechnologyUniversity of HoustonHoustonUSA

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