Article

The role of transparency in recommender systems

Authors:

Rashmi Sinha,

Kirsten SwearingenAuthors Info & Claims

CHI EA '02: CHI '02 Extended Abstracts on Human Factors in Computing Systems

Pages 830 - 831

https://doi.org/10.1145/506443.506619

Published: 20 April 2002 Publication History

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Abstract

Recommender Systems act as a personalized decision guides, aiding users in decisions on matters related to personal taste. Most previous research on Recommender Systems has focused on the statistical accuracy of the algorithms driving the systems, with little emphasis on interface issues and the user's perspective. The goal of this research was to examine the role of transprency (user understanding of why a particular recommendation was made) in Recommender Systems. To explore this issue, we conducted a user study of five music Recommender Systems. Preliminary results indicate that users like and feel more confident about recommendations that they perceive as transparent.

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Yamauchi JHatanaka T(2024)Human-Robot Collaboration: From the Perspective of Systems and Control人とロボットの協調制御：システム制御の観点からJournal of the Robotics Society of Japan10.7210/jrsj.42.9942:2(99-104)Online publication date: 2024
https://doi.org/10.7210/jrsj.42.99
Strikovic EBlassnig SMitova EUrman AEsser Fde Vreese C(2024)Opportunity structures for user acceptance of news recommender systems (NRS): A multi-country survey study of relationships between individual-level factors and evaluations of NRSNew Media & Society10.1177/14614448241263765Online publication date: 26-Jul-2024
https://doi.org/10.1177/14614448241263765
Wang YLiu WYao M(2024)Which recommendation system do you trust the most? Exploring the impact of perceived anthropomorphism on recommendation system trust, choice confidence, and information disclosureNew Media & Society10.1177/14614448231223517Online publication date: 23-Jan-2024
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Information

Published In

CHI EA '02: CHI '02 Extended Abstracts on Human Factors in Computing Systems

April 2002

488 pages

ISBN:1581134541

DOI:10.1145/506443

Conference Chairs:
Loren Terveen
AT&T Labs and University of Minnesota
,
Dennis Wixon
Microsoft

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

New York, NY, United States

Publication History

Published: 20 April 2002

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CHI02

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SIGCHI

CHI02: Human Factors in Computing Systems

April 20 - 25, 2002

Minnesota, Minneapolis, USA

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

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Yamauchi JHatanaka T(2024)Human-Robot Collaboration: From the Perspective of Systems and Control人とロボットの協調制御：システム制御の観点からJournal of the Robotics Society of Japan10.7210/jrsj.42.9942:2(99-104)Online publication date: 2024
https://doi.org/10.7210/jrsj.42.99
Strikovic EBlassnig SMitova EUrman AEsser Fde Vreese C(2024)Opportunity structures for user acceptance of news recommender systems (NRS): A multi-country survey study of relationships between individual-level factors and evaluations of NRSNew Media & Society10.1177/14614448241263765Online publication date: 26-Jul-2024
https://doi.org/10.1177/14614448241263765
Wang YLiu WYao M(2024)Which recommendation system do you trust the most? Exploring the impact of perceived anthropomorphism on recommendation system trust, choice confidence, and information disclosureNew Media & Society10.1177/14614448231223517Online publication date: 23-Jan-2024
https://doi.org/10.1177/14614448231223517
Li SPrasad Majumder BMcAuley J(2024)Self-Supervised Bot Play for Transcript-Free Conversational Critiquing with RationalesACM Transactions on Recommender Systems10.1145/36655023:1(1-20)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3665502
Lubos STran TFelfernig APolat Erdeniz SLe VBoratto LGena CMarras MGermanakos PPopescus E(2024)LLM-generated Explanations for Recommender SystemsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3665185(276-285)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3665185
Schmidt JPointner BMiksch S(2024)Visual Analytics for Understanding Draco's Knowledge BaseIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332691230:1(392-402)Online publication date: 1-Jan-2024
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Chenfu SShoji YYamamoto TTanaka KDürst M(2024)Generating Experiential Descriptions and Estimating Evidence Using Generative Language Model and User Products Reviews2024 IEEE International Conference on Big Data and Smart Computing (BigComp)10.1109/BigComp60711.2024.00047(254-261)Online publication date: 18-Feb-2024
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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
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Liu WWang Y(2024)Evaluating Trust in Recommender Systems: A User Study on the Impacts of Explanations, Agency Attribution, and Product TypesInternational Journal of Human–Computer Interaction10.1080/10447318.2024.2313921(1-13)Online publication date: 14-Feb-2024
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Lim CKim S(2024)Examining factors influencing the user’s loyalty on algorithmic news recommendation serviceHumanities and Social Sciences Communications10.1057/s41599-023-02516-x11:1Online publication date: 2-Jan-2024
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