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| Content Provider | ACM Digital Library |
|---|---|
| Author | Ding, Zhuoye Wu, Yan Huang, Xuanjing Zhang, Qi |
| Abstract | Content reuse is extremely common in user generated mediums. Reuse detection serves as be the basis for many applications. However, along with the explosion of Internet and continuously growing uses of user generated mediums, the task becomes more critical and difficult. In this paper, we present a novel efficient and scalable approach to detect content reuse. We propose a new signature generation algorithm, which is based on learned hash functions for words. In order to deal with tens of billions of documents, we implement the detection approach on graphical processing units (GPUs). The experimental comparison in this paper involves studies of efficiency and effectiveness of the proposed approach in different types of document collections, including ClueWeb09, Tweets2011, and so on. Experimental results show that the proposed approach can achieve the same detection rates with state-of-the-art systems while uses significantly less execution time than them (from 400X to 1500X speedup). |
| Starting Page | 405 |
| Ending Page | 414 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781450314725 |
| DOI | 10.1145/2348283.2348339 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2012-08-12 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Learning to hash Content reuse detection Gpus |
| Content Type | Text |
| Resource Type | Article |
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