1. A method, comprising: causing, at least in part, storing at an apparatus metadata values for each of one or more content offerings, each offering having metadata identifying a primary metagenre, a sub-genre of said primary metagenre, a secondary metagenre, one or more recommenders, and rankings assigned to respective metagenres by the recommenders; receiving at the apparatus user preference information identifying one or more metagenres preferred by a user, and one or more preferred sub-genres for each preferred metagenre; generating at the apparatus a first genre match listing of said content offerings, by identifying offerings whose primary and secondary metagenre data match the user's preferences, ordering the offerings with highest ranking first, and scattering the offerings per metagenre and per recommender; generating at the apparatus a second genre match listing of said content offerings, by identifying offerings whose primary metagenre data and corresponding sub-genre data match the user's preferences, ordering the offerings with highest ranking first, and scattering the offerings per metagenre and per recommender; generating at the apparatus a third genre match listing of content offerings, by identifying offerings whose primary metagenre data matches the user's preferences but whose secondary metagenre data does not match the user's preferences, ordering the offerings with highest ranking first, and scattering the offerings per metagenre and per recommender; and generating at the apparatus a recommendation listing by combining the match listings, the recommendation list eliminates listings having one or more common adjacent metagenres or recommenders, wherein said combining further comprises: creating a first temporary listing via looping each of the match listings to sequentially add entries based on genre; and creating a second temporary listing via looping the first temporary listing to sequentially add entries based on recommender.
9. The method of claim 1 , wherein said content offerings are audio data files, and the recommenders include at least one of users of the list, musical fans, customers of the offerings, music stores, and publishing labels.