1. A method of improving user set recommendations for product advertising comprising the steps of: receiving a request for user set recommendations from any of a set of retailers where such request is related to one or more products; receiving from a plurality of user sets from one or more automated user recommendation systems, wherein the plurality of user sets are generated using different user models; and using ensemble learning to select one or more most relevant user sets from the plurality of user sets.