Thanks for the info. I think your best bet is market basket analysis and looking for the frequently bought baskets and strong relationships between items. A sequential analysis might also help. Neither one of these would be real-time, in that you'd have to already have the frequent itemsets generated and would just be pairing them with the POS items.
So not exactly a recommender in this case, but it might get you some lift-- and that's really all that matters. Yeah-- we do have cash customers, but having the loyalty card helps because knowing what the loyalty customers buy and tracking that history, you can infer to a certain extent what the cash customers may buy. ________________________________________ From: Tim Smith [[email protected]] Sent: Friday, January 10, 2014 8:01 PM To: [email protected] Subject: RE: Item recommendation w/o users or preferences 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: [email protected] > To: [email protected] > 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 [[email protected]] > Sent: Friday, January 10, 2014 5:27 PM > To: [email protected] > 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. 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