@inproceedings{de7aac1ff9964d35adcf0fb3051c7743,
title = "Exploiting spatiotemporal features to infer friendship in location-based social networks",
abstract = "The popularity of smart phone has brought the pervasiveness of location-based social networks. A large number of check-in data provides an opportunity for researchers to infer social ties between users. In this paper, we focus on three problems: (1) how to exploit fine-grained temporal features to characterize people{\textquoteright}s lifestyle. (2) how to use weekday and weekend check-ins data. (3) how to effectively measure the fine-grained location weight. To tackle these problems, we propose a unified framework STIF to infer friendship. Extensive experiments on two real-world location-based datasets show that our proposed STIF framework can significantly outperform the state-of-art methods.",
keywords = "Inferring friendship, Location-based service, Social network, Social ties, Spatiotemporal features",
author = "Cheng He and Chao Peng and Na Li and Xiang Chen and Lanying Guo",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 15th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2018 ; Conference date: 28-08-2018 Through 31-08-2018",
year = "2018",
doi = "10.1007/978-3-319-97310-4\_45",
language = "英语",
isbn = "9783319973098",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "395--403",
editor = "Xin Geng and Byeong-Ho Kang",
booktitle = "PRICAI 2018",
address = "德国",
}