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? On Sun, Aug 29, 2010 at 10:39 AM, Sean Owen <[email protected]> wrote: > "Customers with Similar Searches Purchased" > > To apply user-based CF you need a notion of user-user similarity. You > could think of this as a sub-problem, where users are users and > searches are items, and apply any of the standard UserSimilarity > measures to compute user-user similarity. > > Then, yes this becomes user-based collaborative filtering, but without > ratings. You can just use GenericUserBasedRecommender with your > UserSimilarity. > > That just gets you started -- I think there's room to optimize and > improve on that basic start, such as implementing a custom > UserNeighborhood. > > > "What Do Customers Ultimately Buy After Viewing This Item" > > This isn't really CF, but association rule mining. You might look at > the "Frequent Pattern Mining" support here instead.
