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Systems that adapt to input from users are susceptible to attacks from those users. Recommender systems are common targets for such attacks since there are financial, political and many other motivations for the false promotion or demotion of recommendable items  . Recent research has shown that incorporating trust and reputation models into the recommendation process can have a positive impact on the accuracy of recommendations. In this paper we examine the effect of using five differentdoi:10.1145/1111449.1111476 dblp:conf/iui/ODonovanS06 fatcat:xe5y6bcg4bgspdjnvdt6gbh2oy