extended-abstract

The effect of preference elicitation methods on the user experience of a recommender system

Authors:

Bart P. Knijnenburg,

Martijn C. WillemsenAuthors Info & Claims

CHI EA '10: CHI '10 Extended Abstracts on Human Factors in Computing Systems

Pages 3457 - 3462

https://doi.org/10.1145/1753846.1754001

Published: 10 April 2010 Publication History

Abstract

To increase the user experience, preference elicitation methods used by recommender systems can be adapted to individual differences such as the level of expertise. However, we will show that the satisfaction and perceived usefulness of a recommender system also depends strongly on subtle variations of the implementation of these methods.

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Cited By

Pera MCena FLandoni MMusto CStarke A(2024)Human Factors in User Modeling for Intelligent SystemsA Human-Centered Perspective of Intelligent Personalized Environments and Systems10.1007/978-3-031-55109-3_1(3-42)Online publication date: 1-May-2024
https://doi.org/10.1007/978-3-031-55109-3_1
Chandra SVerma S(2023)Personalized Recommendation During Customer Shopping JourneyThe Palgrave Handbook of Interactive Marketing10.1007/978-3-031-14961-0_32(729-752)Online publication date: 26-Jan-2023
https://doi.org/10.1007/978-3-031-14961-0_32
Cena FConsole LVernero F(2022)How to deal with negative preferences in recommender systems: a theoretical frameworkJournal of Intelligent Information Systems10.1007/s10844-022-00705-960:1(23-47)Online publication date: 26-Apr-2022
https://doi.org/10.1007/s10844-022-00705-9
Show More Cited By

Index Terms

The effect of preference elicitation methods on the user experience of a recommender system

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Information

Published In

cover image ACM Conferences

CHI EA '10: CHI '10 Extended Abstracts on Human Factors in Computing Systems

April 2010

2219 pages

ISBN:9781605589305

DOI:10.1145/1753846

General Chairs:
Elizabeth Mynatt
Georgia Institute of Technology
,
Don Schoner
Georgia Institute of Technology
,
Program Chairs:
Geraldine Fitzpatrick
Vienna University of Technology
,
Scott Hudson
Carnegie Mellon University
,
Keith Edwards
Georgia Tech
,
Tom Rodden
University of Nottingham

Copyright © 2010 Copyright is held by the author/owner(s).

Sponsors

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 April 2010

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Extended-abstract

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CHI '10

Sponsor:

SIGCHI

CHI '10: CHI Conference on Human Factors in Computing Systems

April 10 - 15, 2010

Georgia, Atlanta, USA

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CHI EA '10 Paper Acceptance Rate 350 of 1,346 submissions, 26%;

Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

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Citations

Cited By

Pera MCena FLandoni MMusto CStarke A(2024)Human Factors in User Modeling for Intelligent SystemsA Human-Centered Perspective of Intelligent Personalized Environments and Systems10.1007/978-3-031-55109-3_1(3-42)Online publication date: 1-May-2024
https://doi.org/10.1007/978-3-031-55109-3_1
Chandra SVerma S(2023)Personalized Recommendation During Customer Shopping JourneyThe Palgrave Handbook of Interactive Marketing10.1007/978-3-031-14961-0_32(729-752)Online publication date: 26-Jan-2023
https://doi.org/10.1007/978-3-031-14961-0_32
Cena FConsole LVernero F(2022)How to deal with negative preferences in recommender systems: a theoretical frameworkJournal of Intelligent Information Systems10.1007/s10844-022-00705-960:1(23-47)Online publication date: 26-Apr-2022
https://doi.org/10.1007/s10844-022-00705-9
Munawar ZSuryana NBinti Sa'aya ZHerdiana Y(2020)Framework With An Approach To The User As An Evaluation For The Recommender Systems2020 Fifth International Conference on Informatics and Computing (ICIC)10.1109/ICIC50835.2020.9288565(1-5)Online publication date: 3-Nov-2020
https://doi.org/10.1109/ICIC50835.2020.9288565
Champiri ZFisher BFreund L(2020)rScholar: An Interactive Contextual User Interface to Enhance UX of Scholarly Recommender SystemsHCI International 2020 - Late Breaking Papers: User Experience Design and Case Studies10.1007/978-3-030-60114-0_43(662-686)Online publication date: 3-Oct-2020
https://doi.org/10.1007/978-3-030-60114-0_43
Champiri ZFisher BKiong LDanaee M(2020)How Contextual Data Influences User Experience with Scholarly Recommender Systems: An Empirical FrameworkHCI International 2020 - Late Breaking Papers: User Experience Design and Case Studies10.1007/978-3-030-60114-0_42(635-661)Online publication date: 3-Oct-2020
https://doi.org/10.1007/978-3-030-60114-0_42
Champiri ZMujtaba GSalim SYong Chong C(2019)User Experience and Recommender Systems2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)10.1109/ICOMET.2019.8673410(1-5)Online publication date: Jan-2019
https://doi.org/10.1109/ICOMET.2019.8673410
Donkers TLoepp BZiegler JVassileva JBlustein JAroyo LD'Mello S(2016)Tag-Enhanced Collaborative Filtering for Increasing Transparency and Interactive ControlProceedings of the 2016 Conference on User Modeling Adaptation and Personalization10.1145/2930238.2930287(169-173)Online publication date: 13-Jul-2016
https://dl.acm.org/doi/10.1145/2930238.2930287
Knijnenburg BWillemsen MGantner ZSoncu HNewell C(2012)Explaining the user experience of recommender systemsUser Modeling and User-Adapted Interaction10.1007/s11257-011-9118-422:4-5(441-504)Online publication date: 1-Oct-2012
https://dl.acm.org/doi/10.1007/s11257-011-9118-4
Knijnenburg BWillemsen MKobsa AMobasher BBurke RJannach DAdomavicius G(2011)A pragmatic procedure to support the user-centric evaluation of recommender systemsProceedings of the fifth ACM conference on Recommender systems10.1145/2043932.2043993(321-324)Online publication date: 23-Oct-2011
https://dl.acm.org/doi/10.1145/2043932.2043993
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