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Track: Posters

Paper Title:
User Behavior Oriented Web Spam Detection


Combating Web spam has become one of the top challenges for Web search engines. State-of-the-art spam detection techniques are usually designed for specific known types of Web spam and are incapable and inefficient for recently-appeared spam. With user behavior analyses into Web access logs, we propose a spam page detection algorithm based on Bayes learning. Preliminary experiments on Web access data collected by a commercial Web site (containing over 2.74 billion user clicks in 2 months) show the effectiveness of the proposed detection framework and algorithm.

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