These are just short user histories. Standard item-item cooccurrence analysis should work fine.
If you have any longer histories, that will help even more. On Fri, Jan 10, 2014 at 8:01 PM, Tim Smith <timsmit...@hotmail.com> wrote: > Excellent question. Given who you work for, just assume a customer comes > into a retail location and goes to pay at the checkout. They do not > identity themselves (no loyalty/club card) and use cash (trying to make the > point that we have no idea who this consumer is right at this moment, and > may never will). So rather than having Catalina print out coupons after > the fact, say I want to make an offer right there at the POS during their > transaction. I realize that this is a bit problematic at a grocery store, > but our scenario has a clerk behind a counter with these items close at > hand. So all I have is their current basket and the baskets of previous > anonymous purchases. Clear? > > > From: rachel.ows...@safeway.com > > To: user@mahout.apache.org > > Subject: RE: Item recommendation w/o users or preferences > > Date: Sat, 11 Jan 2014 03:49:53 +0000 > > > > Hi Tim, > > > > By not having user or preference information, it's not clear to me-- do > you mean you have no demographic information, but you have email or some IP > address-- some way to track the user? > > > > It is possible to generate recommendations on purchase history, by > looking at the user's transactions and inferring a preference from what > they buy the most frequently. I used to work for a company that had > transaction history, but it was anonymized-- all the user's activity was > tied to an anonymous token. They didn't even have the name or gender. If > you know a customer's card #, you could relate the card # as their > "user_id" and use the count or monetary value of their transactions for a > specific item as a preference for that item. Try something like conditional > probability-- the probability that you will buy one thing given that you > bought another. By generating a set of pairs (item a being the user has > bought, and item b being the one they have not purchased), you can > determine the probability that they will by item b, given that they bought > item A. > > > > Still, if you know nothing about a person at all, and don't even have a > way to distinguish them on your website, then recommendation won't really > help much because how will you actually give the user recommendations? You > could consider using market basket analysis to tell you what other items a > person might put in his/her cart. I've done market basket analysis before. > It is necessary to do a lot of "pruning" with market basket analysis, > because a lot of the frequent pairs are not very useful. But through some > careful analysis, you may find interesting combinations of items that will > help your business in terms of cross selling/promotion. I am looking at > sequential basket analysis right now. If I buy items x1 through x4, what is > the probability that a certain item will be the next one? You might be able > to use something market basket (fpgrowth) or maybe a markov model to > determine the next item in sequence. > > > > Good luck with this. If you could share the type of data you do have > available, it would be helpful. > > > > Rachel > > ________________________________________ > > From: Tim Smith [timsmit...@hotmail.com] > > Sent: Friday, January 10, 2014 5:27 PM > > To: user@mahout.apache.org > > Subject: Item recommendation w/o users or preferences > > > > Say I have a retail organization that doesn't sell a diverse set of > products, eg 2000, but has many small transactions. Also say that I don't > have any user or preference information. Is it reasonable to use pattern > mining (market baskets) and recommend items based on a set of thresholds > for support, confidence, and lift? If not, what are my options? > > > > "Email Firewall" made the following annotations. > > > ------------------------------------------------------------------------------ > > > > Warning: > > All e-mail sent to this address will be received by the corporate e-mail > system, and is subject to archival and review by someone other than the > recipient. This e-mail may contain proprietary information and is intended > only for the use of the intended recipient(s). If the reader of this > message is not the intended recipient(s), you are notified that you have > received this message in error and that any review, dissemination, > distribution or copying of this message is strictly prohibited. If you > have received this message in error, please notify the sender immediately. > > > > > ============================================================================== > > > >
