This summer I will be a delegate at the AAAI (former American Association for Artificial Intelligence, now Association for the Advancement of Artificial Intelligence) Conference that this year takes place in Vancouver, Canada, 22-26 July.
I will present a paper about Preference-based search in Configurable Catalogs at the workshop about Configuration.
Showing posts with label publications. Show all posts
Showing posts with label publications. Show all posts
Tuesday, May 1, 2007
Friday, April 20, 2007
JAIR article
P. Viappiani, B. Faltings and P. Pu (2006) "Preference-based Search using Example-Critiquing with Suggestions", Journal of Artificial Intelligence Research, Volume 27, pages 465-503
Abstract
We consider interactive tools that help users search for their most
preferred item in a large collection of options. In particular, we
examine example-critiquing, a technique for enabling users to
incrementally construct preference models by critiquing example
options that are presented to them. We present novel techniques for
improving the example-critiquing technology by adding {\em suggestions} to its
displayed options. Such suggestions
are calculated based on an analysis of users' current preference model and
their potential hidden preferences. We evaluate the performance of
our model-based suggestion techniques with both synthetic and
real users. Results show that such suggestions are highly attractive to users
and can stimulate them to express more preferences to improve the chance
of identifying their most preferred item by up to $78\%$.
Read the full paper
Abstract
We consider interactive tools that help users search for their most
preferred item in a large collection of options. In particular, we
examine example-critiquing, a technique for enabling users to
incrementally construct preference models by critiquing example
options that are presented to them. We present novel techniques for
improving the example-critiquing technology by adding {\em suggestions} to its
displayed options. Such suggestions
are calculated based on an analysis of users' current preference model and
their potential hidden preferences. We evaluate the performance of
our model-based suggestion techniques with both synthetic and
real users. Results show that such suggestions are highly attractive to users
and can stimulate them to express more preferences to improve the chance
of identifying their most preferred item by up to $78\%$.
Read the full paper
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