They also proposed two major categories of meta-learning approaches for recommender systems , merging and ensemble , each subdivided into the more specific subclasses suggested by Burke ( 2002 ) as follows .
hybrid recommender systems user modeling and useradapted interaction
R Burke
Lekakos and Giaglis ( 2007 ) implemented a supervised version of this approach for a recommender system , as opposed to our unsupervised version . They also proposed two major categories of meta-learning approaches for recommender systems , merging and ensemble , each subdivided into the more specific subclasses suggested by Burke ( 2002 ) as follows . The merging category corresponds to techniques where the individual methods affect each other in different ways ( this category encompasses Burke 's feature combination , cascade , feature augmentation , and meta-level sub-categories ) .
They also proposed two major categories of meta-learning approaches for recommender systems , merging and ensemble , each subdivided into the more specific subclasses suggested by @@CITATION as follows .