Hi - I've written a recommender algorithm in mahout that unifies recommendations made in different stages of the recommendation process. The idea being recommendations from run 1 are used in a new data model to generate recommendations on different data where the recommendations from the first run are used as user entries as opposed to item based entries.
Something like First Run (UserSet1,ItemSet1) outputs R1 Second Run I take R1 to generate new recommendations as such (R1,ItemSet2) I was curious, is there a defined or preferred method for doing this with mahout?
