The article for this weeks discussion focuses on concentrated efforts to influence an items rating.

My proposed solution to deal with “trolls” influencing recommender systems, would be a system using popularity model vs average rating; if the normalized rating falls outside of 2 standard deviations (the item is too good or bad to be true), the item would fall into another dataset; the item would be given an internal neautral rating (2.5, .5, 5) and would then only be used via a content-based recSys. e.g. “Because you liked movies featuring ISIS and Santa clause, here is episode 17 of South Parks season 6”

The only example I can think of slightly related, would be the Dub the dew event hosted by mountain Dew, where users were asked to recommend a name for their new apple flavored mountain dew product line. Users with malintent made sure the no.1 recommendation would be “Hitler did nothing wrong”