Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

10-2011

Abstract

We study the problem of online classification of user generated content, with the goal of efficiently learning to categorize content generated by individual user. This problem is challenging due to several reasons. First, the huge amount of user generated content demands a highly efficient and scalable classification solution. Second, the categories are typically highly imbalanced, i.e., the number of samples from a particular useful class could be far and few between compared to some others (majority class). In some applications like spam detection, identification of the minority class often has significantly greater value than that of the majority class. Last but not least, when learning a classification model from a group of users, there is a dilemma: A single classification model trained on the entire corpus may fail to capture personalized characteristics such as language and writing styles unique to each user. On the other hand, a personalized model dedicated to each user may be inaccurate due to the scarcity of training data, especially at the very beginning; when users have written just a few articles. To overcome these challenges, we propose learning a global model over all users' data, which is then leveraged to continuously refine the individual models through a collaborative online learning approach. The class imbalance problem is addressed via a cost-sensitive learning approach. Experimental results show that our method is effective and scalable for timely classification of user generated content.

Keywords

online learning, classification, imbalanced class distribution

Discipline

Computer Sciences | Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

CIKM '11: Proceedings of the 20th ACM International Conference on Information and Knowledge Management: Glasgow, Scotland, October 24-28

First Page

285

Last Page

290

ISBN

9781450307178

Identifier

10.1145/2063576.2063622

Publisher

ACM

City or Country

New York

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/2063576.2063622

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