research-article

An approach to controlling user models and personalization effects in recommender systems

Authors:

Marie-Jean Meurs,

Birgitta König-Ries,

Greg Butler, and

Adrian TsangAuthors Info & Claims

IUI '13: Proceedings of the 2013 international conference on Intelligent user interfaces

March 2013

Pages 49 - 56

https://doi.org/10.1145/2449396.2449405

Published: 19 March 2013 Publication History

Abstract

Personalization nowadays is a commodity in a broad spectrum of computer systems. Examples range from online shops recommending products identified based on the user's previous purchases to web search engines sorting search hits based on the user browsing history. The aim of such adaptive behavior is to help users to find relevant content easier and faster. However, there are a number of negative aspects of this behavior. Adaptive systems have been criticized for violating the usability principles of direct manipulation systems, namely controllability, predictability, transparency, and unobtrusiveness. In this paper, we propose an approach to controlling adaptive behavior in recommender systems. It allows users to get an overview of personalization effects, view the user profile that is used for personalization, and adjust the profile and personalization effects to their needs and preferences. We present this approach using an example of a personalized portal for biochemical literature, whose users are biochemists, biologists and genomicists. Also, we report on a user study evaluating the impacts of controllable personalization on the usefulness, usability, user satisfaction, transparency, and trustworthiness of personalized systems.

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Cited By

Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/3652891Online publication date: 13-Apr-2024
https://doi.org/10.1145/3652891
Al-Hazwani ILuo TInel ORicci FEl-Assady MBernard J(2024)ScrollyPOI: A Narrative-Driven Interactive Recommender System for Points-of-Interest Exploration and ExplainabilityAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3665183(292-304)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3665183
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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Index Terms

An approach to controlling user models and personalization effects in recommender systems
1. Applied computing
  1. Document management and text processing
    1. Document preparation
      1. Multi / mixed media creation
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Hypertext / hypermedia

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Information & Contributors

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Published In

cover image ACM Conferences

IUI '13: Proceedings of the 2013 international conference on Intelligent user interfaces

March 2013

470 pages

ISBN:9781450319652

DOI:10.1145/2449396

General Chair:
Jihie Kim
University of Southern California, USA
,
Program Chairs:
Jeffrey Nichols
IBM Research -- Almaden, USA
,
Pedro Szekely
University of Southern California, USA

Copyright © 2013 ACM.

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Published: 19 March 2013

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IUI '13

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IUI '13: 18th International Conference on Intelligent User Interfaces

March 19 - 22, 2013

California, Santa Monica, USA

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IUI '13 Paper Acceptance Rate 43 of 192 submissions, 22%;

Overall Acceptance Rate 746 of 2,811 submissions, 27%

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Cited By

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https://doi.org/10.1145/3652891
Al-Hazwani ILuo TInel ORicci FEl-Assady MBernard J(2024)ScrollyPOI: A Narrative-Driven Interactive Recommender System for Points-of-Interest Exploration and ExplainabilityAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3665183(292-304)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3665183
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
Brusilovsky P(2023)AI in Education, Learner Control, and Human-AI CollaborationInternational Journal of Artificial Intelligence in Education10.1007/s40593-023-00356-z34:1(122-135)Online publication date: 21-Aug-2023
https://doi.org/10.1007/s40593-023-00356-z
Rahdari BKveton BBrusilovsky PBellogín ABoratto LCena F(2022)The Magic of Carousels: Single vs. Multi-List Recommender SystemsProceedings of the 33rd ACM Conference on Hypertext and Social Media10.1145/3511095.3531278(166-174)Online publication date: 28-Jun-2022
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Sankaran SMarkopoulos P(2021)”It’s like a puppet master”: User Perceptions of Personal Autonomy when Interacting with Intelligent TechnologiesProceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450613.3456820(108-118)Online publication date: 21-Jun-2021
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Reguera-Bakhache DGaritano ICernuda CUribeetxeberria RZurutuza ULasa G(2021)An Adaptive Industrial Human-Machine Interface to Optimise Operators Working Performance2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)10.1109/AIM46487.2021.9517434(1213-1219)Online publication date: 12-Jul-2021
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