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Each to his own: how different users call for different interaction methods in recommender systems

Authors:

Bart P. Knijnenburg,

Niels J.M. Reijmer,

Martijn C. WillemsenAuthors Info & Claims

RecSys '11: Proceedings of the fifth ACM conference on Recommender systems

Pages 141 - 148

https://doi.org/10.1145/2043932.2043960

Published: 23 October 2011 Publication History

Abstract

This paper compares five different ways of interacting with an attribute-based recommender system and shows that different types of users prefer different interaction methods. In an online experiment with an energy-saving recommender system the interaction methods are compared in terms of perceived control, understandability, trust in the system, user interface satisfaction, system effectiveness and choice satisfaction. The comparison takes into account several user characteristics, namely domain knowledge, trusting propensity and persistence. The results show that most users (and particularly domain experts) are most satisfied with a hybrid recommender that combines implicit and explicit preference elicitation, but that novices and maximizers seem to benefit more from a non-personalized recommender that just displays the most popular items.

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Cited By

Kehat RHirschprung RAlkoby S(2024)Enhancing User Acceptance of an AI Agent’s Recommendation in Information-Sharing EnvironmentsApplied Sciences10.3390/app1417787414:17(7874)Online publication date: 4-Sep-2024
https://doi.org/10.3390/app14177874
Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/36528913:2(1-68)Online publication date: 13-Apr-2024
https://dl.acm.org/doi/10.1145/3652891
Feng KKoo XTan LBruckman AMcDonald DZhang A(2024)Mapping the Design Space of Teachable Social Media Feed ExperiencesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642120(1-20)Online publication date: 11-May-2024
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Index Terms

Each to his own: how different users call for different interaction methods in recommender systems

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Published In

cover image ACM Conferences

RecSys '11: Proceedings of the fifth ACM conference on Recommender systems

October 2011

414 pages

ISBN:9781450306836

DOI:10.1145/2043932

General Chairs:
Bamshad Mobasher
DePaul University, USA
,
Robin Burke
DePaul University, USA
,
Program Chairs:
Dietmar Jannach
TU Dortmund, Germany
,
Gediminas Adomavicius
University of Minnesota, USA

Copyright © 2011 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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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: 23 October 2011

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RecSys '11

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SIGCHI

RecSys '11: Fifth ACM Conference on Recommender Systems

October 23 - 27, 2011

Illinois, Chicago, USA

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Kehat RHirschprung RAlkoby S(2024)Enhancing User Acceptance of an AI Agent’s Recommendation in Information-Sharing EnvironmentsApplied Sciences10.3390/app1417787414:17(7874)Online publication date: 4-Sep-2024
https://doi.org/10.3390/app14177874
Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/36528913:2(1-68)Online publication date: 13-Apr-2024
https://dl.acm.org/doi/10.1145/3652891
Feng KKoo XTan LBruckman AMcDonald DZhang A(2024)Mapping the Design Space of Teachable Social Media Feed ExperiencesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642120(1-20)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642120
Kleemann TLoepp BZiegler J(2024)Knowledge Graph-Based Integration of Conversational Advisors and Faceted FilteringInteracting with Computers10.1093/iwc/iwae044Online publication date: 18-Sep-2024
https://doi.org/10.1093/iwc/iwae044
Wang JHuang CYan SXie WHe D(2024)When Young Scholars Cooperate with LLMs in Academic Tasks: The Influence of Individual Differences and Task ComplexitiesInternational Journal of Human–Computer Interaction10.1080/10447318.2024.2352919(1-16)Online publication date: 20-May-2024
https://doi.org/10.1080/10447318.2024.2352919
Ziegler JLoepp B(2024)Interactive Recommendation SystemsHandbook of Human Computer Interaction10.1007/978-3-319-27648-9_54-1(1-29)Online publication date: 11-Feb-2024
https://doi.org/10.1007/978-3-319-27648-9_54-1
Torkamaan HTahaei MBuijsman SXiao ZWilkinson DKnijnenburg B(2024)The Role of Human-Centered AI in User Modeling, Adaptation, and Personalization—Models, Frameworks, and ParadigmsA Human-Centered Perspective of Intelligent Personalized Environments and Systems10.1007/978-3-031-55109-3_2(43-84)Online publication date: 1-May-2024
https://doi.org/10.1007/978-3-031-55109-3_2
Afridi AOlsson T(2023)Review of User Interface-Facilitated Serendipity in Recommender SystemsInternational Journal of Interactive Communication Systems and Technologies10.4018/IJICST.32018012:1(1-19)Online publication date: 17-Mar-2023
https://dl.acm.org/doi/10.4018/IJICST.320180
Jin YChen LCai WZhao X(2023)CRS-Que: A User-Centric Evaluation Framework for Conversational Recommender SystemsACM Transactions on Recommender Systems10.1145/3631534Online publication date: 2-Nov-2023
https://dl.acm.org/doi/10.1145/3631534
Lee BDowney DLo KWeld D(2023)LIMEADE: From AI Explanations to Advice TakingACM Transactions on Interactive Intelligent Systems10.1145/358934513:4(1-29)Online publication date: 28-Mar-2023
https://dl.acm.org/doi/10.1145/3589345
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