Title: Predicting by Analogy: A New Way
to
Enhance Business
Abstract: In a
global
world, where e-business is rapidly growing, everyone tries to improve
his
income. Among all the available strategies, trying to predict the
customer’s
behavior and to anticipate his expectations is a solution. To achieve
this
goal, one has to design a model of customer’s behavior, usually by
mining
the tremendous amount of data stored on the company’s computer. In
fact,
the data mining process implicitly assumes the homogeneity of the
context
where data are collected. But the cultural background is different when
an
Egyptian citizen buys with Amazon or when it is a Chinese citizen. We
cannot
suggest a Chinese citizen to buy a spoon after buying a bowl: stick
would
be more appropriate. In that context, the classical techniques of
machine
learning or data mining can be challenged. This is why we suggest
investigating
the use of analogical learning, which allows highlighting hidden
correspondences
between diverse contexts. Analogical learning is not new but we provide
a
set theoretic approach, very suitable for practical implementation.
This
way to model customer behavior could enhance the customer experience in
a
business environment.
Authors: Gilles
Richard, Hasan Alsaedy, and Muhammad Farmer