Personally, I'd look at how to model the purchase events inside a finite
state model that records the probability of each event in a limited
context of past events. In other words, I'd try compressing the
sequence of events, and then suggesting what the compression model says
it's more likely to happen.
Just my 2 cents,
Andres.
-----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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