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Online rating systems play vital role in the recommendation systems and deeply influence the following user choice. It's common occurrence that spammers contaminate the rating systems with malicious rates. However, most of the researchers pay more attention to the accuracy and capability of user preference prediction, rather than the authenticity to rating systems, which provide basis and resource to the recommendation systems. Taking advantage of recommendation algorithm research achievement, we propose a collaborative filtering based spammer detection and user reputation estimation method which perfectly resolves the sparsity problem in huge rating data and promotes poor user preference property of item-based user reputation algorithms. The experiment shows effectiveness of the algorithm both in traditional mode of spammer attack and newly proposed one in this paper, which highly simulates the behavior of real word spammers.

Collaborative Filtering Based Spammer Detection and User Reputation Estimation in Rating System