2015 MultiTaskLearningforSpatioTempo

• (Zhao et al., 2015) ⇒ Liang Zhao, Qian Sun, Jieping Ye, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. (2015). “Multi-Task Learning for Spatio-Temporal Event Forecasting.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783377

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222015%22+Multi-Task+Learning+for+Spatio-Temporal+Event+Forecasting
• http://dl.acm.org/citation.cfm?id=2783258.2783377&preflayout=flat#citedby

Quotes

Author Keywords

• Dynamic query expansion; event forecasting; hard thresholding; lasso; multi-task learning; natural language processing

Abstract

Spatial event forecasting from social media is an important problem but encounters critical challenges, such as dynamic patterns of features (keywords) and geographic heterogeneity (e.g., spatial correlations, imbalanced samples, and different populations in different locations). Most existing approaches (e.g., LASSO regression, dynamic query expansion, and burst detection) are designed to address some of these challenges, but not all of them. This paper proposes a novel multi-task learning framework which aims to concurrently address all the challenges. Specifically, given a collection of locations (e.g., cities), we propose to build forecasting models for all locations simultaneously by extracting and utilizing appropriate shared information that effectively increases the sample size for each location, thus improving the forecasting performance. We combine both static features derived from a predefined vocabulary by domain experts and dynamic features generated from dynamic query expansion in a multi-task feature learning framework; we investigate different strategies to balance homogeneity and diversity between static and dynamic terms. Efficient algorithms based on Iterative Group Hard Thresholding are developed to achieve efficient and effective model training and prediction. Extensive experimental evaluations on Twitter data from four different countries in Latin America demonstrated the effectiveness of our proposed approach.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 MultiTaskLearningforSpatioTempo	Jieping Ye Naren Ramakrishnan Feng Chen Chang-Tien Lu Qian Sun Liang Zhao			Multi-Task Learning for Spatio-Temporal Event Forecasting				10.1145/2783258.2783377		2015