Abstract
Recently, social ties inferring in spatiotemporal data has attracted widespread attentions. Previous studies, which focused on either co-occurrence or context, do not fully exploit the information of spatiotemporal data. In order to better use the spatiotemporal information, in this paper we introduce two novel co-occurrence feature, namely, topic co-occurrence feature and context co-occurrence feature. The former feature is extracted by the topic model on carefully constructed bag-of-words. The latter feature is extracted by natural language processing tools on carefully constructed context sequence, which considers context, co-occurrence and mobility periodicity simultaneously. These two novel co-occurrence feature are both based on time and space perspectives. Then we infer social ties from these multi-view co-occurrence feature (including baseline co-occurrence, topic and context co-occurrence). The experiments demonstrate that the two novel co-occurrence feature contribute to the social tie inferring significantly.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Machanavajjhala, A., Korolova, A., Sarma, A.: Personalized social recommendations: accurate or private. Proc. VLDB Endow. 4, 440–450 (2011)
Crandall, D., Backstrom, L., Cosley, D., Suri, S., Huttenlocher, D., Kleinberg, J.: Inferring social ties from geographic coincidences. In: Proceedings of the National Academy of Sciences of America, pp. 22436–22441 (2010)
Pham, H., Shahabi, C., Liu, Y.: EBM: An entropy-based model to infer social strength from spatiotemporal data. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, pp. 265–276 (2013)
Zhou, N., Zhang, X., Wang, S.: Theme-aware social strength inference from spatiotemporal data. In: Li, F., Li, G., Hwang, S.-w, Yao, B., Zhang, Z. (eds.) WAIM 2014. LNCS, vol. 8485, pp. 498–509. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08010-9_56
Njoo, G.S., Kao, M.-C., Hsu, K.-W., Peng, W.-C.: Exploring check-in data to infer social ties in location based social networks. In: Kim, J., Shim, K., Cao, L., Lee, J.-G., Lin, X., Moon, Y.-S. (eds.) PAKDD 2017. LNCS (LNAI), vol. 10234, pp. 460–471. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57454-7_36
Bagci, H., Karagoz, P.: Context-aware friend recomendation for location based social networks using random walk. In: Proceedings of the 25th International Conference Companion on World Wide Web, pp. 531–536 (2016)
Chen, X., Pang J. and Xue R.: Constructing and comparing user mobility profiles for location-based services. In: Proceedings of the 28th Annual ACM Symposium on Applied Computing, pp. 261–266 (2013)
Chen, X., Kordy, P., Lu, R., Pang, J.: MinUS: mining user similarity with trajectory patterns. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds.) ECML PKDD 2014. LNCS (LNAI), vol. 8726, pp. 436–439. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44845-8_29
Chen, X., Lu, R., Ma, X., Pang, J.: Measuring user similarity with trajectory patterns: principles and new metrics. In: Chen, L., Jia, Y., Sellis, T., Liu, G. (eds.) APWeb 2014. LNCS, vol. 8709, pp. 437–448. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11116-2_38
Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1082–1090 (2011)
Huang, Y., Zhu, F., Yuan, M., et al.: Telco churn prediction with big data [C]. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp. 607–618. ACM (2015)
Liu, G., Nguyen, T.T., Zhao, G., et al.: Repeat buyer prediction for e-commerce [C]. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 155–164. ACM (2016)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation [J]. J. Mach. Learn. Res., pp. 993–1022 (2003)
Zeng, J., Cheung, W.K., Liu, J.: Learning topic models by belief propagation[J]. IEEE Trans. Pattern Anal. Mach. Intell., pp. 121–1134 (2013)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient Estimation of Word Representations in Vector Space (2013)
Mikolov, T., Le, Q.V., Sutskever, I.: Exploiting Similarities among Languages for Machine Translation (2013)
Leskovec, J., Krevl, A.: SNAP Datasets: Stanford Large Network Dataset Collection (2014)
Chen, T., Guestrin, C.: XGBoost: A Scalable Tree Boosting System (2016)
Bradley, A.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. In: Pattern Recognition. (1997)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Xu, C., Bai, R. (2018). Inferring Social Ties from Multi-view Spatiotemporal Co-occurrence. In: U, L., Xie, H. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 11268. Springer, Cham. https://doi.org/10.1007/978-3-030-01298-4_31
Download citation
DOI: https://doi.org/10.1007/978-3-030-01298-4_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01297-7
Online ISBN: 978-3-030-01298-4
eBook Packages: Computer ScienceComputer Science (R0)