User-item shilling attacks affect highly-active and slightly-active users in opposite directions depending on dataset sparsity, with highly-active users more vulnerable on sparse Yelp and slightly-active users more vulnerable on denser MovieLens.
Breese, David Heckerman, and Carl Myers Kadie
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Assessing the Impact of a User-Item Collaborative Attack on Class of Users
User-item shilling attacks affect highly-active and slightly-active users in opposite directions depending on dataset sparsity, with highly-active users more vulnerable on sparse Yelp and slightly-active users more vulnerable on denser MovieLens.