Maintaining preference networks that adapt to changing preferences

  1. Get@NRC: Maintaining preference networks that adapt to changing preferences (Opens in a new window)
DOIResolve DOI:
AuthorSearch for: ; Search for: ; Search for:
TypeBook Chapter
Proceedings titleAdvances in Artificial Intelligence : 26th Canadian Conference on Artificial Intelligence, Canadian AI 2013, Regina, SK, Canada, May 28-31, 2013. Proceedings
Series titleLecture Notes In Computer Science; Volume 7884
Conference26th Canadian Conference on Artificial Intelligence (Canadian AI 2013), May 28-31, 2013, Regina, Saskatchewan, Canada
Pages8999; # of pages: 11
SubjectAmount of information; Automated decision making; Dense graphs; Preference elicitation; Preference graph; Transitive reductions; User Modeling; Algorithms; Decision making; Artificial intelligence
AbstractDecision making can be more difficult with an enormous amount of information, not only for humans but also for automated decision making processes. Although most user preference elicitation models have been developed based on the assumption that user preferences are stable, user preferences may change in the long term and may evolve with experience, resulting in dynamic preferences. Therefore, in this paper, we describe a model called the dynamic preference network (DPN) that is maintained using an approach that does not require the entire preference graph to be rebuilt when a previously-learned preference is changed, with efficient algorithms to add new preferences and to delete existing preferences. DPNs are shown to outperform existing algorithms for insertion, especially for large numbers of attributes and for dense graphs. They do have some shortcomings in the case of deletion, but only when there is a small number of attributes or when the graph is particularly dense. © 2013 Springer-Verlag.
Publication date
PublisherSpringer Berlin Heidelberg
AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedYes
NPARC number21270631
Export citationExport as RIS
Report a correctionReport a correction
Record identifier105af4dc-24f7-454e-9c2d-c3f30cabd9ee
Record created2014-02-17
Record modified2016-07-04
Bookmark and share
  • Share this page with Facebook (Opens in a new window)
  • Share this page with Twitter (Opens in a new window)
  • Share this page with Google+ (Opens in a new window)
  • Share this page with Delicious (Opens in a new window)