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’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.
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Naive Bayes corresponds to Naive Bayes, Neural Network corresponds to Multilayer perceptron (MLP), KNN corresponds to IBK, Decision Tree (C4.5) corresponds to J48, Random Forest corresponds to Random Forest in WEKA, respectively.
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He, C., Peng, C., Li, N., Chen, X., Guo, L. (2018). Exploiting Spatiotemporal Features to Infer Friendship in Location-Based Social Networks. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11013. Springer, Cham. https://doi.org/10.1007/978-3-319-97310-4_45
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DOI: https://doi.org/10.1007/978-3-319-97310-4_45
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