Yes, one time tested way to do this is the "apriori" algo which looks at frequent item sets and creates rules.
I was looking for a shortcut using a recommender, which would be super easy to try. The rule builder is a little harder to implement but we can also test precision on that and compare the two. The recommender method below should be reasonable AFAICT except for the method(s) of retrieving recs, which seem likely to be slow. On Feb 14, 2013, at 9:45 AM, Sean Owen <sro...@gmail.com> wrote: This sounds like a job for frequent item set mining, which is kind of a special case of the ideas you've mentioned here. Given N items in a cart, which next item most frequently occurs in a purchased cart? On Thu, Feb 14, 2013 at 6:30 PM, Pat Ferrel <pat.fer...@gmail.com> wrote: > I thought you might say that but we don't have the add-to-cart action. We > have to calculate cart purchases by matching cart IDs or session IDs. So we > only have cart purchases with items. > > If we had the add-to-cart and the purchase we could use your cross-action > method for getting recs by training only on those two actions. > > Still without the add-to-cart the method below should work, right? The > main problem being finding a similar cart in the training set quickly. Are > there other problems? > > On Feb 14, 2013, at 9:19 AM, Ted Dunning <ted.dunn...@gmail.com> wrote: > > I think that this is an excellent use case for cross recommendation from > cart contents (items) to cart purchases (items). The cross aspect is that > the recommendation is from two different kinds of actions, not two kinds of > things. The first action is insertion into a cart and the second is > purchase of an item. > > On Thu, Feb 14, 2013 at 9:53 AM, Pat Ferrel <pat.fer...@gmail.com> wrote: > >> There are several methods for recommending things given a shopping cart >> contents. At the risk of using the same tool for every problem I was >> thinking about a recommender's use here. >> >> I'd do something like train on shopping cart purchases so row = cartID, >> column = itemID. >> Given cart contents I could find the most similar cart in the training > set >> by using a similarity measure then get recs for this closest matched > cart. >> >> The search for similar carts may be slow if I have to check for pairwise >> similarity so I could cluster and find the best cluster then search it > for >> the best cart. I could create a decision tree on all trained carts and > walk >> as far as I can down the tree to find the cart with the most > cooccurrences. >> There may be other cooccurrence based methods in mahout??? With the id of >> the cart I can then get recs from the training set. I could also fold-in >> the new cart contents to the training set and ask for recs based on it >> (this seems like it would take a long time to compute). This last would >> also pollute the trained matrix with partial carts over time. >> >> This seems like another place where Lucene might help but are there other >> mahout methods to look at before I diving into Lucene? > >
