Article

Learning user preferences for sets of objects

Authors:

Marie desJardins,

Kiri L. WagstaffAuthors Info & Claims

ICML '06: Proceedings of the 23rd international conference on Machine learning

Pages 273 - 280

https://doi.org/10.1145/1143844.1143879

Published: 25 June 2006 Publication History

Abstract

Most work on preference learning has focused on pairwise preferences or rankings over individual items. In this paper, we present a method for learning preferences over sets of items. Our learning method takes as input a collection of positive examples---that is, one or more sets that have been identified by a user as desirable. Kernel density estimation is used to estimate the value function for individual items, and the desired set diversity is estimated from the average set diversity observed in the collection. Since this is a new learning problem, we introduce a new evaluation methodology and evaluate the learning method on two data collections: synthetic blocks-world data and a new real-world music data collection that we have gathered.

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Cited By

Liu KChen YTang JHuang HLiu L(2023)Self-Attentive Subset Learning over a Set-Based Preference in RecommendationApplied Sciences10.3390/app1303168313:3(1683)Online publication date: 28-Jan-2023
https://doi.org/10.3390/app13031683
Balog KRadlinski FArakelyan SPiwowarski BChevalier MGaussier EMaarek YNie JScholer F(2019)Transparent, Scrutable and Explainable User Models for Personalized RecommendationProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331211(265-274)Online publication date: 18-Jul-2019
https://dl.acm.org/doi/10.1145/3331184.3331211
Girau RPilloni VAtzori L(2018)The virtual user: The holistic manager of our IoT applications2018 IEEE 4th World Forum on Internet of Things (WF-IoT)10.1109/WF-IoT.2018.8355115(149-154)Online publication date: Feb-2018
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Learning user preferences for sets of objects

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cover image ACM Other conferences

ICML '06: Proceedings of the 23rd international conference on Machine learning

June 2006

1154 pages

ISBN:1595933832

DOI:10.1145/1143844

Program Chairs:
William Cohen,
Andrew Moore

Copyright © 2006 ACM.

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]

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Association for Computing Machinery

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Publication History

Published: 25 June 2006

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ICML '06 Paper Acceptance Rate 140 of 548 submissions, 26%;

Overall Acceptance Rate 140 of 548 submissions, 26%

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Cited By

Liu KChen YTang JHuang HLiu L(2023)Self-Attentive Subset Learning over a Set-Based Preference in RecommendationApplied Sciences10.3390/app1303168313:3(1683)Online publication date: 28-Jan-2023
https://doi.org/10.3390/app13031683
Balog KRadlinski FArakelyan SPiwowarski BChevalier MGaussier EMaarek YNie JScholer F(2019)Transparent, Scrutable and Explainable User Models for Personalized RecommendationProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331211(265-274)Online publication date: 18-Jul-2019
https://dl.acm.org/doi/10.1145/3331184.3331211
Girau RPilloni VAtzori L(2018)The virtual user: The holistic manager of our IoT applications2018 IEEE 4th World Forum on Internet of Things (WF-IoT)10.1109/WF-IoT.2018.8355115(149-154)Online publication date: Feb-2018
https://doi.org/10.1109/WF-IoT.2018.8355115
Memon MLi JMemon IArain Q(2017)GEO matching regionsMultimedia Tools and Applications10.1007/s11042-016-3834-z76:14(15377-15411)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1007/s11042-016-3834-z
Moretti SÖztürk MTsoukiàs A(2016)Preference ModellingMultiple Criteria Decision Analysis10.1007/978-1-4939-3094-4_3(43-95)Online publication date: 2016
https://doi.org/10.1007/978-1-4939-3094-4_3
Tzitzikas YPapadakos P(2013)Interactive Exploration of Multi-Dimensional and Hierarchical Information Spaces with Real-Time Preference ElicitationFundamenta Informaticae10.5555/2594857.2594862122:4(357-399)Online publication date: 1-Oct-2013
https://dl.acm.org/doi/10.5555/2594857.2594862
Pashalidis APreneel B(2012)Evaluating Tag-Based Preference Obfuscation SystemsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2011.11824:9(1613-1623)Online publication date: 1-Sep-2012
https://dl.acm.org/doi/10.1109/TKDE.2011.118
Clémençon SGaudel RJakubowicz J(2011)Clustering rankings in the fourier domainProceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I10.5555/2034063.2034098(343-358)Online publication date: 5-Sep-2011
https://dl.acm.org/doi/10.5555/2034063.2034098
Mason MLopes MBillard AKahn PAdams JTrafton G(2011)Robot self-initiative and personalization by learning through repeated interactionsProceedings of the 6th international conference on Human-robot interaction10.1145/1957656.1957814(433-440)Online publication date: 6-Mar-2011
https://dl.acm.org/doi/10.1145/1957656.1957814
Marques VCosta ANovais P(2011)A dynamic user profiling technique in a AmI environment2011 World Congress on Information and Communication Technologies10.1109/WICT.2011.6141427(1247-1252)Online publication date: Dec-2011
https://doi.org/10.1109/WICT.2011.6141427
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