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| Content Provider | ACM Digital Library |
|---|---|
| Author | Fan, Shaokun Liu, Hengchang Zhang, Kunpeng Ouksel, Aris M. |
| Abstract | People are using social media to generate, share, and communicate information with each other. Finding actionable insights from such big data has attracted a lot of research attentions on, for example, finding targeted user groups based on their historical on-line activities. However, existing ma- chine learning algorithms fail to keep up with the increasing large data volume. In this paper, we develop a scalable regression-based algorithm called distributed iterative shrinkage-thresholding algorithm (DISTA) that can identify potential users. Our experiments conducted on Facebook data containing billions of users and associated activities show that DISTA with feature selection not only enables on-line audience-targeted approach for precise marketing but also performs efficiently on parallel computers. |
| Starting Page | 341 |
| Ending Page | 344 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781450326681 |
| DOI | 10.1145/2645710.2645763 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2014-10-06 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Feature selection Dista Social brand Advertising |
| Content Type | Text |
| Resource Type | Article |
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