2015 DirichletHawkesProcesseswithApp

• (Du et al., 2015) ⇒ Nan Du, Mehrdad Farajtabar, Amr Ahmed, Alexander J. Smola, and Le Song. (2015). “Dirichlet-Hawkes Processes with Applications to Clustering Continuous-Time Document Streams.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783411

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222015%22+Dirichlet-Hawkes+Processes+with+Applications+to+Clustering+Continuous-Time+Document+Streams
• http://dl.acm.org/citation.cfm?id=2783258.2783411&preflayout=flat#citedby

Quotes

Author Keywords

• Dirichlet process; document modeling; hawkes process; metrics; miscellaneous

Abstract

Clusters in document streams, such as online news articles, can be induced by their textual contents, as well as by the temporal dynamics of their arriving patterns. Can we leverage both sources of information to obtain a better clustering of the documents, and distill information that is not possible to extract using contents only? In this paper, we propose a novel random process, referred to as the Dirichlet-Hawkes process, to take into account both information in a unified framework. A distinctive feature of the proposed model is that the preferential attachment of items to clusters according to cluster sizes, present in Dirichlet processes, is now driven according to the intensities of cluster-wise self-exciting temporal point processes, the Hawkes processes. This new model establishes a previously unexplored connection between Bayesian Nonparametrics and temporal Point Processes, which makes the number of clusters grow to accommodate the increasing complexity of online streaming contents, while at the same time adapts to the ever changing dynamics of the respective continuous arrival time. We conducted large-scale experiments on both synthetic and real world news articles, and show that Dirichlet-Hawkes processes can recover both meaningful topics and temporal dynamics, which leads to better predictive performance in terms of content perplexity and arrival time of future documents.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 DirichletHawkesProcesseswithApp	Alexander J. Smola Nan Du Amr Ahmed Le Song Mehrdad Farajtabar			Dirichlet-Hawkes Processes with Applications to Clustering Continuous-Time Document Streams				10.1145/2783258.2783411		2015