NDLI: Search engine click spam detection based on bipartite graph propagation

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Search engine click spam detection based on bipartite graph propagation

Content Provider	ACM Digital Library
Author	Zhang, Min Ru, Liyun Li, Xin Ma, Shaoping Liu, Yiqun Jin, Yijiang
Abstract	Using search engines to retrieve information has become an important part of people's daily lives. For most search engines, click information is an important factor in document ranking. As a result, some websites cheat to obtain a higher rank by fraudulently increasing clicks to their pages, which is referred to as "Click Spam". Based on an analysis of the features of fraudulent clicks, a novel automatic click spam detection approach is proposed in this paper, which consists of 1. modeling user sessions with a triple sequence, which, to the best of our knowledge, takes into account not only the user action but also the action objective and the time interval between actions for the first time; 2. using the user-session bipartite graph propagation algorithm to take advantage of cheating users to find more cheating sessions; and 3. using the pattern-session bipartite graph propagation algorithm to obtain cheating session patterns to achieve higher precision and recall of click spam detection. Experimental results based on a Chinese commercial search engine using real-world log data containing approximately 80 million user clicks per day show that 2.6% of all clicks were detected as spam with a precision of up to 97%.
Starting Page	93
Ending Page	102
Page Count	10
File Format	PDF
ISBN	9781450323512
DOI	10.1145/2556195.2556214
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-02-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Frequent sequential patterns Click spam Label propagation User session model
Content Type	Text
Resource Type	Article

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