The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Exploiting User Check-In Data for Geo-Friend Recommendations in Location-Based Social Networks
|
|
Author(s): Shudong Liu (School of Information and Security Engineering, Zhongnan University of Economics and Law, Wuhan, China)and Ke Zhang (School of Information and Security Engineering, Zhongnan University of Economics and Law, China)
Copyright: 2020
Volume: 11
Issue: 2
Pages: 17
Source title:
International Journal of Mobile Computing and Multimedia Communications (IJMCMC)
Editor(s)-in-Chief: Agustinus Waluyo (Monash University, Australia)
DOI: 10.4018/IJMCMC.2020040101
Purchase
|
Abstract
The development of Web 2.0 technologies has meant that online social networks can both help the public facilitate sharing and communication and help them make new friends through their cyberspace social circles. Generating more accurate and geographically related results to help users find more friends in real life is gradually becoming a research hotspot. Recommending geographically related friends and alleviating check-in data sparsity problems in location-based social networks allows those to divide a day into different time slots and automatically collect user check-in data at each time slot over a certain period. Second, some important location points or regions are extracted from raw check-in trajectories, temporal periodic trajectories are constructed, and a geo-friend recommendation framework is proposed that can help users find geographically related friends. Finally, empirical studies from a real-world dataset demonstrate that this paper's method outperforms other existing methods for geo-friend recommendations in location-based social networks.
Related Content
|
Wanqiao Wang, Jian Su, Hui Zhang, Luyao Guan, Qingrong Zheng, Zhuofan Tang, Huixia Ding.
© 2024.
16 pages.
|
|
.
© 2024.
|
|
Xinhong You, Pengping Zhang, Minglin Liu, Lingqi Lin, Shuai Li.
© 2023.
18 pages.
|
|
Nan Zhao, Jiaye Wang, Bo Jin, Ru Wang, Minghu Wu, Yu Liu, Lufeng Zheng.
© 2023.
17 pages.
|
|
Tongyao Nie, Xinguang Lv.
© 2023.
14 pages.
|
|
Ali Bonyadi Naeini, Ali Golbazi Mahdipour, Rasam Dorri.
© 2023.
24 pages.
|
|
Agnitè Maxim Wilfrid Straiker Edoh, Tahirou Djara, Abdou-Aziz Sobabe Ali Tahirou, Antoine Vianou.
© 2023.
16 pages.
|
|
|