Campus Event Calendar
Event Entry
What and Who
Collaborative Ranking Processes
Joachim Giesen
Ringvorlesung
AG 1, AG 2, AG 3, AG 4, AG 5, SWS
Public Audience
English
Note: We use this to send email in the morning.
Date, Time and Location
Thursday, 25 January 2007
12:30
60 Minutes
Warning: Time has changed!
E1 4
023
Saarbrücken
Warning: New Room!
Abstract
I will consider the problem of estimating product preferences for
substitute goods or services, i.e., goods or services that serve the
same purpose, from a population of consumers. Here preferences are
elicited by questionnaires that pose a few choice tasks to
individuals from the population (respondents). Preference
elicitation by choice tasks is considered accurate since as it tries
to simulate real buying situations. The simplest choice task is a
pairwise comparison. To elicit a respondent's ranking of n
products completely n log(n) pairwise comparisons are
necessary. These are easily too many in settings where the
incentive for the respondent is not high though he might be willing
to answer a few questions truthfully. The standard approach to cope
with this complexity in marketing research is to aggregate the
answers of several (or all) respondents in order to estimate an
individual's complete preference ranking. I will describe such an
aggregation mechanism based on spectral clustering and prove its
validity in statistical model of population and respondents.
Joint work with Dieter Mitsche and Eva Schuberth (both from ETH Zuerich)
substitute goods or services, i.e., goods or services that serve the
same purpose, from a population of consumers. Here preferences are
elicited by questionnaires that pose a few choice tasks to
individuals from the population (respondents). Preference
elicitation by choice tasks is considered accurate since as it tries
to simulate real buying situations. The simplest choice task is a
pairwise comparison. To elicit a respondent's ranking of n
products completely n log(n) pairwise comparisons are
necessary. These are easily too many in settings where the
incentive for the respondent is not high though he might be willing
to answer a few questions truthfully. The standard approach to cope
with this complexity in marketing research is to aggregate the
answers of several (or all) respondents in order to estimate an
individual's complete preference ranking. I will describe such an
aggregation mechanism based on spectral clustering and prove its
validity in statistical model of population and respondents.
Joint work with Dieter Mitsche and Eva Schuberth (both from ETH Zuerich)
Contact
--email hidden
passcode not visible
logged in users only
Veronika Weinand, 01/23/2007 09:05
Uwe Brahm, 01/22/2007 18:01
Veronika Weinand, 11/21/2006 10:42
Silke Wagner, 11/06/2006 16:17
Veronika Weinand, 11/06/2006 14:39
Veronika Weinand, 11/03/2006 12:21
Veronika Weinand, 10/25/2006 15:08
Veronika Weinand, 10/24/2006 12:47
Veronika Weinand, 10/23/2006 15:06
Veronika Weinand, 10/19/2006 14:54
Uwe Brahm, 01/22/2007 18:01
Veronika Weinand, 11/21/2006 10:42
Silke Wagner, 11/06/2006 16:17
Veronika Weinand, 11/06/2006 14:39
Veronika Weinand, 11/03/2006 12:21
Veronika Weinand, 10/25/2006 15:08
Veronika Weinand, 10/24/2006 12:47
Veronika Weinand, 10/23/2006 15:06
Veronika Weinand, 10/19/2006 14:54