Subjective Ranking Algorithms: Modeling Customer’s
Experience
There are have been many challenges posted by continuous
explosive availability of computers and the internet in terms of humans’
interacting with information:
1.
The excess amount of information has made a
difficult and time consuming task the end users processing and filtering
through the available information to find what the users are looking for.
2.
For information suppliers such as advertisers, the
above challenge becomes how to understand and model the algorithms and
process behind the end users’ search for information? The end users would
always like to receive the most likely results to satisfy their query first and
in a highlighted format as well.
3.
Conjoint analysis ( originated in mathematical
psychology and was developed by marketing professor Paul
Green at the Wharton School of the University of Pennsylvania and Data Chan in
1070’s, s (Carroll & Green, 1995), (Green & Srinivasan, 1978),
(Green & Srinivasan, 1990)) try to understand and model customer
experience with a controlled experimental
design and statistical analysis: experimentally controlled combinations of
attribute levels called profiles are presented to respondents for evaluation
(ratings or rankings). In a multiple
regression analysis these evaluations then become the dependent variables
predicted as a function of the experimental design variables manifested in the
profiles.
4.
While conjoint analysis data are more of “forward
looking” compared to traditional linear regression analysis of “historical” data,
its controlled format isolates and ignores the real time and real life
streaming feature of data end users in
actual environment.
5.
6.
7.
8.
when the users search for structured,
multi‐attribute products, documents or other artifacts of information.
When the user searches for the ideal multi‐attribute product, like a car
configuration, a restaurant with ratings, an LCD screen or other electronics,
with multiple specifications, and other complex purchasing decisions, where
price is not the only driving factor, the users expect to receive the candidate
products in the order of their preference. Also, when a user reads
through a long article on their smart phone, they prefer to read a summary in
the smaller screen, rather than a long multi‐page article. When
the user expects to read a summarized article, they again expect to receive the
important information to them first. There are many similar
problems such as these ones, where the information overload problem can be
alleviated with intelligent ranking algorithms, so that the human user gets to
the right information first.
PDF]Multidomain Demand Modeling in Design for Market Systems
https://deepblue.lib.umich.edu/.../nwkang...
University of Michigan Library
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by N Kang - 2014 - Cited by 4 - Related articles
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[PDF]9 Things Clients Get Wrong about Conjoint Analysis
research.google.com/pubs/archive/41886.pdf
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