Lies and Propaganda: Detecting Spam Users in Collaborative Filtering

摘要

Collaborative Filtering systems are essentially social systems which make recommendations using 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, or downplaying the popularity of an item. 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.