2013 CollaborativeBoostingforActivit

• (Song et al., 2013) ⇒ Yangqiu Song, Zhengdong Lu, Cane Wing-ki Leung, and Qiang Yang. (2013). “Collaborative Boosting for Activity Classification in Microblogs.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487661

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Collaborative+Boosting+for+Activity+Classification+in+Microblogs
• http://dl.acm.org/citation.cfm?id=2487575.2487661&preflayout=flat#citedby

Quotes

Author Keywords

• Activity classification; boosting; collaborative classification; learning; models; social regularization.

Abstract

Users' daily activities, such as dining and shopping, inherently reflect their habits, intents and preferences, thus provide invaluable information for services such as personalized information recommendation and targeted advertising. Users' activity information, although ubiquitous on social media, has largely been unexploited. This paper addresses the task of user activity classification in microblogs, where users can publish short messages and maintain social networks online. We identify the importance of modeling a user's individuality, and that of exploiting opinions of the user's friends for accurate activity classification. In this light, we propose a novel collaborative boosting framework comprising a text-to-activity classifier for each user, and a mechanism for collaboration between classifiers of users having social connections. The collaboration between two classifiers includes exchanging their own training instances and their dynamically changing labeling decisions. We propose an iterative learning procedure that is formulated as gradient descent in learning function space, while opinion exchange between classifiers is implemented with a weighted voting in each learning iteration. We show through experiments that on real-world data from Sina Weibo, our method outperforms existing off-the-shelf algorithms that do not take users' individuality or social connections into account.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 CollaborativeBoostingforActivit	Qiang Yang Yangqiu Song Zhengdong Lu Cane Wing-ki Leung			Collaborative Boosting for Activity Classification in Microblogs				10.1145/2487575.2487661		2013