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Rating Web Pages Using Page-Transition Evidence

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8233)

Abstract

The rating of web pages is an important metric that has wide applications, such as web search and malicious page detection. Existing solutions for web page rating rely on either subjective opinions or overall page relationships. In this paper, we present a new solution, SnowEye, to decide the trust rating of web pages with evidence obtained from browsers. The intuition of our approach is that user-activated page transition behaviors provide dynamic evidence to evaluate the rating of web pages. We present an algorithm to rate web pages based on page transitions triggered by users.We prototyped our approach in the Google Chrome browser. Our evaluation through real-world websites and simulation supports our intuition and verifies the correctness of our approach.

Keywords

Trust Rating Target Page Page Transition Dynamic Evidence Intuitive Requirement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.School of Electronic and Information EngineeringBeiHang UniversityChina
  2. 2.School of ComputingNational University of SingaporeSingapore
  3. 3.Institute of Computer Science and TechnologyPeking UniversityChina

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