Construction and Reduction Methods of Web Spam Identification Index System

Yuancheng       Li; Rong       Huang; Xiangqian       Nie

doi:10.2174/2213275912666181127130120

Abstract

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.

Keywords: Autoencoder, index reduction, stacked autoencoder neural network, web spam, identification index system, detection model.

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DOI https://dx.doi.org/10.2174/2213275912666181127130120	Print ISSN 2213-2759
Publisher Name Bentham Science Publisher	Online ISSN 1874-4796

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