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From Tim Smith <>
Subject RE: Item recommendation w/o users or preferences
Date Sat, 11 Jan 2014 04:01:00 GMT
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:
> To:
> 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 []
> Sent: Friday, January 10, 2014 5:27 PM
> To:
> 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?
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