Abstract
The full coverage of Wi-Fi signals and the popularization of intelligent card systems provide a large volume of data that contain human mobility patterns. Effectively utilizing such data to make user behavior predictions finds useful applications such as predictive behavior analysis, personalized recommendation, and location-aware services. Existing methods for user behavior predictions merely capture temporal dependencies within individual historical records. We argue that user behaviors are largely affected by friends in their social circles and such influences are dynamic due to users’ dynamic social behaviors. In this paper, we propose a model named SDSIM which consists of three independent and complementary modules to jointly model the influences of user dynamic social behaviors, user demographics similarities, and individual-level behavior patterns. We construct time-evolving graphs to indicate user dynamic social behaviors and design a novel component named DSBcell which captures not only the social influences but also the regularity and periodicity of user social behaviors. We also construct a graph based on user similarities in demographics and generate a representation for each user. Experiments on two real-world datasets for multiple user behavior-related prediction tasks prove the effectiveness of our proposed model compared with state-of-the-art methods.
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
Bai, T., Zhang, S., Egleston, B.L., Vucetic, S.: Interpretable representation learning for healthcare via capturing disease progression through time. In: SIGKDD, pp. 43–51 (2018)
Bi, W., Kwok, J.T.: Multi-label classification on tree-and dag-structured hierarchies. In: ICML, pp. 17–24 (2011)
Braytee, A., Liu, W., Catchpoole, D.R., Kennedy, P.J.: Multi-label feature selection using correlation information. In: CIKM, pp. 1649–1656 (2017)
Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: Spectral networks and locally connected networks on graphs. In: ICLR (2014)
Chen, C., et al.: Predictive analysis by leveraging temporal user behavior and user embeddings. In: CIKM, pp. 2175–2182 (2018)
Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: SIGKDD, pp. 1082–1090 (2011)
Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: NIPS, pp. 3844–3852 (2016)
Guo, S., Lin, Y., Feng, N., Song, C., Wan, H.: Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In: AAAI, pp. 922–929 (2019)
Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In: ICLR (2018)
Liu, H., Zhu, Y., Xu, Y.: Learning from heterogeneous student behaviors for multiple prediction tasks. In: Nah, Y., Cui, B., Lee, S.-W., Yu, J.X., Moon, Y.-S., Whang, S.E. (eds.) DASFAA 2020. LNCS, vol. 12113, pp. 297–313. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59416-9_18
Ma, F., Chitta, R., Zhou, J., You, Q., Sun, T., Gao, J.: Dipole: diagnosis prediction in healthcare via attention-based bidirectional recurrent neural networks. In: SIGKDD, pp. 1903–1911 (2017)
Monti, F., Boscaini, D., Masci, J., Rodola, E., Svoboda, J., Bronstein, M.M.: Geometric deep learning on graphs and manifolds using mixture model CNNs. In: CVPR, pp. 5115–5124 (2017)
Niepert, M., Ahmed, M., Kutzkov, K.: Learning convolutional neural networks for graphs. In: ICML, pp. 2014–2023 (2016)
Pareja, A., et al.: EvolveGCN: Evolving graph convolutional networks for dynamic graphs. In: AAAI (2020)
Pi, Q., Bian, W., Zhou, G., Zhu, X., Gai, K.: Practice on long sequential user behavior modeling for click-through rate prediction. In: SIGKDD, pp. 2671–2679 (2019)
Seo, Y., Defferrard, M., Vandergheynst, P., Bresson, X.: Structured sequence modeling with graph convolutional recurrent networks. In: Cheng, L., Leung, A.C.S., Ozawa, S. (eds.) ICONIP 2018. LNCS, vol. 11301, pp. 362–373. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04167-0_33
Su, Y., et al.: Exercise-enhanced sequential modeling for student performance prediction. In: AAAI, pp. 2435–2443 (2018)
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: Large-scale information network embedding. In: WWW, pp. 1067–1077 (2015)
Wang, P., et al.: Understanding convolution for semantic segmentation. In: WACV, pp. 1451–1460 (2018)
Wen, Y.T., Yeh, P.W., Tsai, T.H., Peng, W.C., Shuai, H.H.: Customer purchase behavior prediction from payment datasets. In: WSDM, pp. 628–636 (2018)
Yang, G., Cai, Y., Reddy, C.K.: Recurrent spatio-temporal point process for check-in time prediction. In: CIKM, pp. 2203–2211 (2018)
Yang, G., Cai, Y., Reddy, C.K.: Spatio-temporal check-in time prediction with recurrent neural network based survival analysis. In: IJCAI, pp. 2976–2983 (2018)
Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: IJCAI, pp. 3634–3640 (2018)
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. In: ICLR (2016)
Yuan, Q., Cong, G., Ma, Z., Sun, A., Thalmann, N.M.: Who, where, when and what: discover spatio-temporal topics for twitter users. In: SIGKDD, pp. 605–613 (2013)
Yuan, Q., Zhang, W., Zhang, C., Geng, X., Cong, G., Han, J.: PRED: Periodic region detection for mobility modeling of social media users. In: WSDM, pp. 263–272 (2017)
Zhang, Y., Pennacchiotti, M.: Predicting purchase behaviors from social media. In: WWW, pp. 1521–1532 (2013)
Zhang, Y., et al.: Sequential click prediction for sponsored search with recurrent neural networks. In: AAAI, pp. 1369–1375 (2014)
Zhu, Y., et al.: What to do next: modeling user behaviors by time-LSTM. In: IJCAI, pp. 3602–3608 (2017)
Acknowledgments
This research is supported in part by the 2030 National Key AI Program of China 2018AAA0100503 (2018AAA0100500), National Science Foundation of China (No. 62072304, No. 61772341, No. 61472254), Shanghai Municipal Science and Technology Commission (No. 18511103002, No. 19510760500, and No. 19511101500), the Program for Changjiang Young Scholars in University of China, the Program for China Top Young Talents, the Program for Shanghai Top Young Talents, SJTU Global Strategic Partnership Fund (2019 SJTU-HKUST), the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University (No. SL2020MS032) and Scientific Research Fund of Second Institute of Oceanography (No. SL2020MS032).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Zang, T., Zhu, Y., Gong, C., Liu, H., Li, B. (2021). Modeling Dynamic Social Behaviors with Time-Evolving Graphs for User Behavior Predictions. In: Jensen, C.S., et al. Database Systems for Advanced Applications. DASFAA 2021. Lecture Notes in Computer Science(), vol 12681. Springer, Cham. https://doi.org/10.1007/978-3-030-73194-6_35
Download citation
DOI: https://doi.org/10.1007/978-3-030-73194-6_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-73193-9
Online ISBN: 978-3-030-73194-6
eBook Packages: Computer ScienceComputer Science (R0)