1. A method for partitioning a plurality of items into groups of similar items, said plurality of items corresponding to a selection history by at least one third party, said method comprising the steps of: partitioning said third party selection history into k clusters; identifying at least one mean item for each of said k clusters; and assigning each of said plurality of items to one of said clusters based on a distance metric.