I have some understanding now. So given 2 matrices user * (page view/search term) and user * (purchased item), how do you connect these 2 matrices given that I can define the user or item sim methods?
Also, can the second use case can be solved with CF or association mining is needed? Pramit On Mon, Aug 30, 2010 at 12:07 AM, Sean Owen <[email protected]> wrote: > Yes, this is a simpler problem. You just want to find which items are > most similar to a given item, for some definition of 'similar'. > GenericItemBasedRecommender has a mostSimilarItems() method that just > saves you the trouble of computing this by hand, and any > ItemSimiliarity function you like can be used. > > On Sun, Aug 29, 2010 at 7:26 PM, Ted Dunning <[email protected]> > wrote: > > These are examples of what I call cross-recommendation where you have > user x > > item1 and user x item2 data and you > > want item1 => item2 recommendations. > > > > All of the standard techniques apply (user-based, LLR cooccurrence, SVD, > > latent factor models), but you have to rejigger things here > > and there. > > > > Sean, can Mahout's recommendation system do this cross recommendation? > > > -- Thanks, Pramit
