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. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]: Search process, H.3.4 [Systems and Software]: Performance evaluation General Terms Experimentation Keywords Spam detection, Web search engine, User behavior analysis

• Type: Conference
• Year: 2008
• Where: WWW
• Authors: Yiqun Liu, Min Zhang, Shaoping Ma, Liyun Ru
• Keywords: Internet Technology, Keywords Spam Detection, Spam Page Detection, State-of-the-art Spam Detection, WWW 2008