NDLI: Lies and propaganda: detecting spam users in collaborative filtering

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Lies and propaganda: detecting spam users in collaborative filtering

Content Provider	ACM Digital Library
Author	Fankhauser, Peter Hofmann, Thomas Mehta, Bhaskar
Abstract	Collaborative Filtering systems are essentially social systems which base their recommendation on the judgment of a large number of people. However, like other social systems, they are also vulnerable to manipulation by malicious social elements. Lies and Propaganda may be spread by a malicious user who may have an interest in promoting an item, or downplaying the popularity of another one. By doing this systematically, with either multiple identities, or by involving more people, a few malicious user votes and profiles can be injected into a collaborative recommender system. This can significantly affect the robustness of a system or algorithm, as has been studied in recent work [5, 7]. While current detection algorithms are able to use certain characteristics of spam profiles to detect them, they suffer from low precision, and require a large amount of training data. In this work, we provide a simple unsupervised algorithm, which exploits statistical properties of effective spam profiles to provide a highly accurate and fast algorithm for detecting spam.
Starting Page	14
Ending Page	21
Page Count	8
File Format	PDF
ISBN	1595934812
DOI	10.1145/1216295.1216307
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2007-01-28
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Dimensionality reduction Information filtering Data mining Spam detection
Content Type	Text
Resource Type	Article

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