I find that the Book "artificial Intelligence A modern approach 2nd
edition" by Stuart Russell and Peter Norvig can offer some good
approaches take. Page 344 section 10.5 deals with the problem of finding
relevant offers. Which when you think about it is very similar to your
problem you are solving. So It would get you on the right track for you
solution I would think.
You could write rules for extracting the person likes and interests from
what they buy and correlate them to the product categories and perform
another analysis to take the top ten picks etc.
That section and the following one 10.6 reasoning systems for categories
could be directly applied.
HTH,
Sean Glazier
Cincom Systems
Lead Engineer
-----Original Message-----
From:
vwnc-bounces@... [mailto:
vwnc-bounces@...] On
Behalf Of Rick Flower
Sent: Tuesday, June 24, 2008 9:21 AM
To:
vwnc@...
Subject: [vwnc] Algorithms for future purchase predictions for an
orderingsystem?
Hi all..
One of the areas I'll be working on this summer for a Seaside based
web-app has to do with making suggestions for ordering products based on
past buying habits (taking into account perhaps the past 4-5 years).
The
current people-driven system (no computers helping currently) works on a
demand schedule -- if people ask for product X then that product is
ordered -- causing people to wait for delivery. What I want to do is
look
back in time perhaps a month in advance (say for the Christmas period)
and
have the system make suggestions for products to buy that have been
popular for the past several years. That way when people put in their
orders, the product is sitting and waiting for them instead of the other
way around.
To that end, I could certainly generate some sort of algorithm to look
at
purchase history for a selected time period and make suggestions..
However, I figured it might be best to ask if anyone knows of an
algorithm
that tries to solve this equation from the get-go... Anyway, if no such
algorithm exists then I'll brute-force it and write my own but thought
I'd
ask first.. Thx!
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