Article

Modeling user preferences via theory refinement

Authors:

Ben Geisler,

Vu Ha,

Peter HaddawyAuthors Info & Claims

IUI '01: Proceedings of the 6th international conference on Intelligent user interfaces

Pages 87 - 90

https://doi.org/10.1145/359784.360291

Published: 01 January 2001 Publication History

Get Access

Abstract

We present an approach to elicitation of user preference models in which assumptions can be used to guide but not constrain the elicitation process. We show how to encode assumptions concerning preferential independence and monotonicity in a Knowledge-Based Artificial Neural Network. We quantify the degree to which user preferences violate a set of assumptions. We empirically compare the KBANN network with an unbiased ANN in terms of learning rate and accuracy for preferences consistent and inconsistent with the assumptions. We go on to demonstrate how the technique can be used to learn a fine-grained preference structure from simple binary classification data.

References

[1]

Chajewska, U., Koller, D., Parr, R. Making rational decision using adaptive utility elicitation, in Proc. AAAI-00, Aug 2000, pp 363-369.

Digital Library

Google Scholar

[2]

Ha, V. & Haddawy, P. Toward case-based preference elicitation: Similarity measures on preference structures, in Proc. UAI98 (July 1998), pp 193-201.

Digital Library

Google Scholar

[3]

Keeney, R. L., and Raiffa, H. Decisions with Multiple Objectives: Preferences and Value Tradeoffs. Wiley, New York, 1976.

Google Scholar

[4]

Nguyen, H. & Haddawy, P. The decision-theoretic interactive video advisor, in Proc. UAI99 (Aug 1999), pp 494-501.

Digital Library

Google Scholar

[5]

Shavlik, J. & Towell, G. An approach to combining explanation-based and neural learning algorithms. Connection Science, 1(3): 233-255, 1989.

Crossref

Google Scholar

[6]

J. Shavlik, S. Calcari, T. Eliassi-Rad, & J. Solock. An Instructable, Adaptive Interface for Discovering and Monitoring Information on the World-Wide Web, in Proc. IUI-99.

Digital Library

Google Scholar

Cited By

View all

Law MRusso ABroda K(2019)Logic-Based Learning of Answer Set ProgramsReasoning Web. Explainable Artificial Intelligence10.1007/978-3-030-31423-1_6(196-231)Online publication date: 13-Sep-2019
https://doi.org/10.1007/978-3-030-31423-1_6
LAW MRUSSO ABRODA K(2015)Learning weak constraints in answer set programmingTheory and Practice of Logic Programming10.1017/S147106841500019815:4-5(511-525)Online publication date: 3-Sep-2015
https://doi.org/10.1017/S1471068415000198
Molinero CAndrés CNúñez MGabillon ASheng QMansoor W(2010)A Formal Methodology to Specify Hierarchical Agent-Based SystemsWeb-Based Information Technologies and Distributed Systems10.2991/978-94-91216-32-9_5(93-114)Online publication date: 2010
https://doi.org/10.2991/978-94-91216-32-9_5
Show More Cited By

Index Terms

Modeling user preferences via theory refinement

Recommendations

Preference elicitation via theory refinement

We present an approach to elicitation of user preference models in which assumptions can be used to guide but not constrain the elicitation process. We demonstrate that when domain knowledge is available, even in the form of weak and somewhat inaccurate ...
The local geometry of multiattribute tradeoff preferences

Existing representations for multiattribute ceteris paribus preference statements have provided useful treatments and clear semantics for qualitative comparisons, but have not provided similarly clear representations or semantics for comparisons ...
Web Service Selection with Quantitative and Qualitative User Preferences
WI-IAT '11: Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01

Most existing approaches of Web service selection with user preferences are either quantitative or qualitative. However, using a qualitative or quantitative approach alone cannot handle all the non-functional properties(NFPs). To solve this problem, we ...

Comments

Information & Contributors

Information

Published In

IUI '01: Proceedings of the 6th international conference on Intelligent user interfaces

January 2001

174 pages

ISBN:1581133251

DOI:10.1145/359784

Chairmen:
Candy Sidner
MERL
,
Johanna Moore
Univ. of Edinburgh, Edinburgh, Scotland

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 January 2001

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

IUI01

Sponsor:

IUI01: International Conference on Intelligent User Interfaces 2001

January 14 - 17, 2001

New Mexico, Santa Fe, USA

Acceptance Rates

Overall Acceptance Rate 746 of 2,811 submissions, 27%

Upcoming Conference

IUI '25

Sponsor:
sigai
sigai

30th International Conference on Intelligent User Interfaces

March 24 - 27, 2025

Cagliari , Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
523
Total Downloads

Downloads (Last 12 months)10
Downloads (Last 6 weeks)0

Reflects downloads up to 25 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Law MRusso ABroda K(2019)Logic-Based Learning of Answer Set ProgramsReasoning Web. Explainable Artificial Intelligence10.1007/978-3-030-31423-1_6(196-231)Online publication date: 13-Sep-2019
https://doi.org/10.1007/978-3-030-31423-1_6
LAW MRUSSO ABRODA K(2015)Learning weak constraints in answer set programmingTheory and Practice of Logic Programming10.1017/S147106841500019815:4-5(511-525)Online publication date: 3-Sep-2015
https://doi.org/10.1017/S1471068415000198
Molinero CAndrés CNúñez MGabillon ASheng QMansoor W(2010)A Formal Methodology to Specify Hierarchical Agent-Based SystemsWeb-Based Information Technologies and Distributed Systems10.2991/978-94-91216-32-9_5(93-114)Online publication date: 2010
https://doi.org/10.2991/978-94-91216-32-9_5
Andrés CMolinero CNúñez M(2009)A Hierarchical Methodology to Specify and Simulate Complex Computational SystemsProceedings of the 9th International Conference on Computational Science: Part I10.1007/978-3-642-01970-8_34(347-356)Online publication date: 20-May-2009
https://dl.acm.org/doi/10.1007/978-3-642-01970-8_34
Andrés CMolinero CNúñez M(2008)A Formal Methodology to Specify Hierarchical Agent-Based SystemsProceedings of the 2008 IEEE International Conference on Signal Image Technology and Internet Based Systems10.1109/SITIS.2008.70(169-176)Online publication date: 30-Nov-2008
https://dl.acm.org/doi/10.1109/SITIS.2008.70
Dolbear CHobson PVallet DFernández MCantadorz ICastellsz P(2008)Personalised Multimedia SummariesSemantic Multimedia and Ontologies10.1007/978-1-84800-076-6_6(165-183)Online publication date: 2008
https://doi.org/10.1007/978-1-84800-076-6_6
Luo XJennings NShadbolt N(2006)Acquiring user tradeoff strategies and preferences for negotiating agentsInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2005.08.00164:4(304-321)Online publication date: 1-Apr-2006
https://dl.acm.org/doi/10.1016/j.ijhcs.2005.08.001
Rodríguez IHaddad HOmicini AWainwright RLiebrock L(2004)Formal specification of autonomous commerce agentsProceedings of the 2004 ACM symposium on Applied computing10.1145/967900.968060(774-778)Online publication date: 14-Mar-2004
https://dl.acm.org/doi/10.1145/967900.968060
Luo XJennings NShadbolt NSadeh NDively MKauffman RLabrou YShehory OTelang RCranor L(2003)Knowledge-based acquisition of tradeoff preferences for negotiating agentsProceedings of the 5th international conference on Electronic commerce10.1145/948005.948024(138-149)Online publication date: 30-Sep-2003
https://dl.acm.org/doi/10.1145/948005.948024

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Preference elicitation via theory refinement

The local geometry of multiattribute tradeoff preferences

Web Service Selection with Quantitative and Qualitative User Preferences

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Upcoming Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations