Linking Micro Event History to Macro Prediction in Point Process Models

Yichen Wang, Xiaojing Ye, Haomin Zhou, Hongyuan Zha, Le Song
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR 54:1375-1384, 2017.

Abstract

User behaviors in social networks are microscopic with fine grained temporal information. Predicting a macroscopic quantity based on users’ collective behaviors is an important problem. However, existing works are mainly problem-specific models for the microscopic behaviors and typically design approximation or heuristic algorithms to compute the macroscopic quantity. In this paper, we propose a unifying framework with a jump stochastic differential equation model that systematically links the microscopic event data and macroscopic inference, and the theory to approximate its probability distribution. We showed that our method can better predict the user behaviors in real-world applications.

Cite this Paper

BibTeX
@InProceedings{pmlr-v54-wang17f, title = {{Linking Micro Event History to Macro Prediction in Point Process Models}}, author = {Wang, Yichen and Ye, Xiaojing and Zhou, Haomin and Zha, Hongyuan and Song, Le}, booktitle = {Proceedings of the 20th International Conference on Artificial Intelligence and Statistics}, pages = {1375--1384}, year = {2017}, editor = {Singh, Aarti and Zhu, Jerry}, volume = {54}, series = {Proceedings of Machine Learning Research}, month = {20--22 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v54/wang17f/wang17f.pdf}, url = {https://proceedings.mlr.press/v54/wang17f.html}, abstract = {User behaviors in social networks are microscopic with fine grained temporal information. Predicting a macroscopic quantity based on users’ collective behaviors is an important problem. However, existing works are mainly problem-specific models for the microscopic behaviors and typically design approximation or heuristic algorithms to compute the macroscopic quantity. In this paper, we propose a unifying framework with a jump stochastic differential equation model that systematically links the microscopic event data and macroscopic inference, and the theory to approximate its probability distribution. We showed that our method can better predict the user behaviors in real-world applications.} }
Endnote
%0 Conference Paper %T Linking Micro Event History to Macro Prediction in Point Process Models %A Yichen Wang %A Xiaojing Ye %A Haomin Zhou %A Hongyuan Zha %A Le Song %B Proceedings of the 20th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2017 %E Aarti Singh %E Jerry Zhu %F pmlr-v54-wang17f %I PMLR %P 1375--1384 %U https://proceedings.mlr.press/v54/wang17f.html %V 54 %X User behaviors in social networks are microscopic with fine grained temporal information. Predicting a macroscopic quantity based on users’ collective behaviors is an important problem. However, existing works are mainly problem-specific models for the microscopic behaviors and typically design approximation or heuristic algorithms to compute the macroscopic quantity. In this paper, we propose a unifying framework with a jump stochastic differential equation model that systematically links the microscopic event data and macroscopic inference, and the theory to approximate its probability distribution. We showed that our method can better predict the user behaviors in real-world applications.
APA
Wang, Y., Ye, X., Zhou, H., Zha, H. & Song, L.. (2017). Linking Micro Event History to Macro Prediction in Point Process Models. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 54:1375-1384 Available from https://proceedings.mlr.press/v54/wang17f.html.

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