Construction and Reduction Methods of Web Spam Identification Index System
| Autor: | Yuancheng Li, Xiangqian Nie, Rong Huang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Spamdexing
0209 industrial biotechnology Identification (information) 020901 industrial engineering & automation Information retrieval General Computer Science Index system Computer science 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 02 engineering and technology Reduction methods |
| Zdroj: | Recent Patents on Computer Science. 12:202-211 |
| ISSN: | 2213-2759 |
| DOI: | 10.2174/2213275912666181127130120 |
| Popis: | Background: With the rapid development of the Internet, the number of web spam has increased dramatically in recent years, which has wasted search engine storage and computing power on a massive scale. To identify the web spam effectively, the content features, link features, hidden features and quality features of web page are integrated to establish the corresponding web spam identification index system. However, the index system is highly correlation dimension. Methods: An improved method of autoencoder named stacked autoencoder neural network (SAE) is used to realize the reduction of the web spam identification index system. Results: The experiment results show that our method could reduce effectively the index of web spam and significantly improves the recognition rate in the following work. Conclusion: An autoencoder based web spam indexes reduction method is proposed in this paper. The experimental results show that it greatly reduces the temporal and spatial complexity of the future web spam detection model. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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