Article

Multivariate preference models and decision making with the MAUT machine

Authors:

Christian Schmitt,

Dietmar Dengler,

Mathias BauerAuthors Info & Claims

UM'03: Proceedings of the 9th international conference on User modeling

Pages 297 - 302

Published: 22 June 2003 Publication History

Abstract

With the advent of e-commerce, systems supporting the user in finding just the right product in an electronic catalog have gained inreasing attention. While collaborative recommender systems (RS) derive their suggestions from other users' opinions, structure-based systems assess a product according to how well its properties satisfy a user's preferences. This paper presents the MAUT Machine, a system implementing the basic machinery to be used by a structure-based RS to elicit and maintain complex user preference models and evaluate the entries of an electronic catalog according to their appropriateness for a given user or group of users.

References

[1]

T. Asahi, D. Turo, and B. Shneiderman. Using treemaps to visualize the analytic hierarchy process. Information Systems Research, 6(4):357-375, 1995.

Digital Library

Google Scholar

[2]

R. Fagin and E. Wimmers. A formula for incorporating weights into scoring rules. In Proc. of the International Conference on Database Theory, pages 247-261, 1997.

Google Scholar

[3]

A. Jameson, R. Schafer, J. Simons, and T. Weis. Adaptive provision of evaluation-oriented information: Tasks and techniques. In IJCAI, pages 1886-1895, 1995.

Digital Library

Google Scholar

[4]

G. Linden, S. Hanks, and N. Lesh. Interactive assessment of user preference models: The automated travel assistant. In User Modeling 97, 1997.

Google Scholar

[5]

T.L. Saaty. The Analytic Hierarchy Process. McGraw-Hill, 1980.

Google Scholar

[6]

R. Yager. On ordered weighted averaging aggregation operators in multicriteria decision making. IEEE Transactions On Systems, Man and Cybernetics, 18:183- 190, 1988.

Digital Library

Google Scholar

Cited By

View all

Felfernig ASchippel SLeitner GReinfrank FIsak KMandl MBlazek PNinaus G(2013)Automated repair of scoring rules in constraint-based recommender systemsAI Communications10.5555/2594638.259464126:1(15-27)Online publication date: 1-Jan-2013
https://dl.acm.org/doi/10.5555/2594638.2594641
Andrews P(2012)System Personality and Persuasion in Human-Computer DialogueACM Transactions on Interactive Intelligent Systems10.1145/2209310.22093152:2(1-27)Online publication date: 1-Jun-2012
https://dl.acm.org/doi/10.1145/2209310.2209315
Cocea MMagoulas G(2011)Context-dependent feedback prioritisation in exploratory learning revisitedProceedings of the 19th international conference on User modeling, adaption, and personalization10.5555/2021855.2021862(62-74)Online publication date: 11-Jul-2011
https://dl.acm.org/doi/10.5555/2021855.2021862
Show More Cited By

Recommendations

User preference representation based on psychometric models
ADC '11: Proceedings of the Twenty-Second Australasian Database Conference - Volume 115

Neighbourhood-based collaborative filtering is one of the most popular recommendation techniques, and has been applied successfully in various fields. User ratings are often used by neighbourhood-based collaborative filtering to compute the similarity ...
Preference-based graphic models for collaborative filtering
UAI'03: Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence

Collaborative filtering is a very useful general technique for exploiting the preference patterns of a group of users to predict the utility of items to a particular user. Previous research has studied several probabilistic graphic models for ...
Reducing preference elicitation in group decision making

Reducing preference elicitation when selecting a winner item.Computing approximate winners with some confidence level.Terminating preference elicitation sooner by returning k alternatives.Two preference aggregation strategies: Least Misery and ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

UM'03: Proceedings of the 9th international conference on User modeling

June 2003

436 pages

ISBN:3540403817

Editors:
Peter Brusilovsky
University of Pittsburgh, School of Information Sciences, Department of Information Science and Telecommunications, Pittsburgh, PA
,
Albert Corbett
Carnegie Mellon University, Human Computer Interaction Institute, Pittsburgh, PA
,
Fiorella de Rosis
University of Bari, Intelligent Interfaces, Department of Informatics, Bari, Italy

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 22 June 2003

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
0
Total Downloads

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

Reflects downloads up to 18 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Felfernig ASchippel SLeitner GReinfrank FIsak KMandl MBlazek PNinaus G(2013)Automated repair of scoring rules in constraint-based recommender systemsAI Communications10.5555/2594638.259464126:1(15-27)Online publication date: 1-Jan-2013
https://dl.acm.org/doi/10.5555/2594638.2594641
Andrews P(2012)System Personality and Persuasion in Human-Computer DialogueACM Transactions on Interactive Intelligent Systems10.1145/2209310.22093152:2(1-27)Online publication date: 1-Jun-2012
https://dl.acm.org/doi/10.1145/2209310.2209315
Cocea MMagoulas G(2011)Context-dependent feedback prioritisation in exploratory learning revisitedProceedings of the 19th international conference on User modeling, adaption, and personalization10.5555/2021855.2021862(62-74)Online publication date: 11-Jul-2011
https://dl.acm.org/doi/10.5555/2021855.2021862
Cocea MMagoulas G(2009)Hybrid model for learner modelling and feedback prioritisation in exploratory learningInternational Journal of Hybrid Intelligent Systems10.5555/1735958.17359616:4(211-230)Online publication date: 1-Dec-2009
https://dl.acm.org/doi/10.5555/1735958.1735961
Teppan EFelfernig A(2009)Minimization of Product Utility Estimation Errors in Recommender Result Set EvaluationsProceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 0110.1109/WI-IAT.2009.11(20-27)Online publication date: 15-Sep-2009
https://dl.acm.org/doi/10.1109/WI-IAT.2009.11
Felfernig ATeppan EFriedrich GIsak KStaab S(2008)Intelligent debugging and repair of utility constraint sets in knowledge-based recommender applicationsProceedings of the 13th international conference on Intelligent user interfaces10.1145/1378773.1378802(217-226)Online publication date: 13-Jan-2008
https://dl.acm.org/doi/10.1145/1378773.1378802
Harvey TCarberry SDecker K(2005)Tailored responses for decision supportProceedings of the 10th international conference on User Modeling10.1007/11527886_21(164-168)Online publication date: 24-Jul-2005
https://dl.acm.org/doi/10.1007/11527886_21

View Options

View options

Login options

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

Abstract

References

Cited By

Recommendations

User preference representation based on psychometric models

Preference-based graphic models for collaborative filtering

Reducing preference elicitation in group decision making

Comments

Information

Published In

Publisher

Publication History

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

Login options

Full Access

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations