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 <[email protected]> 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 <[email protected]> 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 <[email protected]> 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? > >
