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| Content Provider | ACM Digital Library |
|---|---|
| Author | Lee, SangKeun Kim, Kang-Min Jun, So-Young Lee, Jung-Hyun Park, So-Jung |
| Abstract | In-app advertising has become a significant source of revenue for mobile app. In order to improve the effectiveness of in-app ads, most ad networks focus on targeting a user based on the user's personal information collected from their ad library inside mobile apps and the global knowledge built from big data on ad servers. However, sharing user's sensitive information with the ad servers may raise privacy concerns. As opposed to targeting users, mobile contextual advertising seeks to target the app page a user is viewing. In this demo, we present a novel mobile contextual advertising platform, called MoCA, which is designed to improve the semantic relevance of in-app ads in a stand-alone, privacy-protecting manner on mobile devices. MoCA understands the semantics of app page and ads, and then matches semantically relevant ads to the page inside mobile devices. To the best of our knowledge, this is the first work to implement the mobile contextual advertising platform based on the semantic approach without resort to ad servers. |
| Starting Page | 181 |
| Ending Page | 181 |
| Page Count | 1 |
| File Format | |
| ISBN | 9781450349284 |
| DOI | 10.1145/3081333.3089326 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2017-06-16 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | In-app advertising Mobile contextual advertising Tiny text intelligence |
| Content Type | Text |
| Resource Type | Article |
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