1. A computer-implemented method, comprising: maintaining a collection of items in association with a user, said collection comprising items rated by the user; applying a clustering algorithm to said collection of items to subdivide the collection into multiple clusters, wherein the clusters are generated based, at least in part, on calculated distances between the items; classifying at least a first cluster of said multiple clusters, based at least partly on the user's ratings of items therein, as representing an area in which the user lacks interest; classifying at least a second cluster of said multiple clusters, based at least partly on the user's ratings of items therein, as representing an area of interest to the user; obtaining a set of recommended items for the user from a recommendation engine; filtering the set of recommended items based, at least in part, on distances between the recommended items and one or more of said clusters of items, wherein filtering the set of recommended items comprises filtering out at least one item from the set of recommended items based on a determination that the recommended item corresponds to an area in which the user lacks interest, said determination based at least partly on both (1) a calculated distance between the recommended item and said first cluster, and (2) a calculated distance between the recommended item and the second cluster, said filtering producing a filtered set of recommended items, said filtering performed automatically by a computer system; and outputting the filtered set of recommended items for presentation to the user.