1. A computer-implemented method, comprising: retrieving item preference data for a target user from computer storage, said item preference data reflective of item preferences of the target user; generating a recommendation set using the item preference data, said recommendation set comprising a computer representation of items predicted to be of interest to the target user; generating a filtered recommendation set by using item similarity data to filter the recommendation set, said filtered recommendation set having a greater degree of item diversity than the recommendation set, wherein generating the filtered recommendation set comprises identifying a fuzzy duplicate item pair in the recommendation set, and removing one item of said fuzzy duplicate item pair from the recommendation set; and outputting information regarding at least some of the items in the filtered recommendation set for presentation to the target user.
8. The method of claim 1 , wherein the recommendation set is generated by a recommendation engine, and the step of generating the filtered recommendation set is performed by a filtering component that is separate from the recommendation engine.