2014 FEMAFlexibleEvolutionaryMultiFa

• (Jiang et al., 2014) ⇒ Meng Jiang, Peng Cui, Fei Wang, Xinran Xu, Wenwu Zhu, and Shiqiang Yang. (2014). “FEMA: Flexible Evolutionary Multi-faceted Analysis for Dynamic Behavioral Pattern Discovery.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623644

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+FEMA%3A+Flexible+Evolutionary+Multi-faceted+Analysis+for+Dynamic+Behavioral+Pattern+Discovery
• http://dl.acm.org/citation.cfm?id=2623330.2623644&preflayout=flat#citedby

Quotes

Author Keywords

• Behavior modeling; behavioral pattern; clustering; evolutionary analysis; flexible regularizers; tensor factorization

Abstract

Behavioral pattern discovery is increasingly being studied to understand human behavior and the discovered patterns can be used in many real world applications such as web search, recommender system and advertisement targeting. Traditional methods usually consider the behaviors as simple user and item connections, or represent them with a static model. In real world, however, human behaviors are actually complex and dynamic: they include correlations between user and multiple types of objects and also continuously evolve along time. These characteristics cause severe data sparsity and computational complexity problem, which pose great challenge to human behavioral analysis and prediction. In this paper, we propose a Flexible Evolutionary Multi-faceted Analysis (FEMA) framework for both behavior prediction and pattern mining. FEMA utilizes a flexible and dynamic factorization scheme for analyzing human behavioral data sequences, which can incorporate various knowledge embedded in different object domains to alleviate the sparsity problem. We give approximation algorithms for efficiency, where the bound of approximation loss is theoretically proved. We extensively evaluate the proposed method in two real datasets. For the prediction of human behaviors, the proposed FEMA significantly outperforms other state-of-the-art baseline methods by 17.4%. Moreover, FEMA is able to discover quite a number of interesting multi-faceted temporal patterns on human behaviors with good interpretability. More importantly, it can reduce the run time from hours to minutes, which is significant for industry to serve real-time applications.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 FEMAFlexibleEvolutionaryMultiFa	Fei Wang Peng Cui Wenwu Zhu Shiqiang Yang Meng Jiang Xinran Xu			FEMA: Flexible Evolutionary Multi-faceted Analysis for Dynamic Behavioral Pattern Discovery				10.1145/2623330.2623644		2014