research-article

PeerChooser: visual interactive recommendation

Authors:

Tobias HöllererAuthors Info & Claims

CHI '08: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Pages 1085 - 1088

https://doi.org/10.1145/1357054.1357222

Published: 06 April 2008 Publication History

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Abstract

Collaborative filtering (CF) has been successfully deployed over the years to compute predictions on items based on a user's correlation with a set of peers. The black-box nature of most CF applications leave the user wondering how the system arrived at its recommendation. This note introduces PeerChooser, a collaborative recommender system with an interactive graphical explanation interface. Users are provided with a visual explanation of the CF process and opportunity to manipulate their neighborhood at varying levels of granularity to reflect aspects of their current requirements. In this manner we overcome the problem of redundant profile information in CF systems, in addition to providing an explanation interface. Our layout algorithm produces an exact, noiseless graph representation of the underlying correlations between users. PeerChooser's prediction component uses this graph directly to yield the same results as the benchmark. User's then improve on these predictions by tweaking the graph to their current requirements. We present a user-survey in which PeerChooser compares favorably against a benchmark CF algorithm.

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Cited By

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Chatti MGuesmi MMuslim A(2024)Visualization for Recommendation Explainability: A Survey and New PerspectivesACM Transactions on Interactive Intelligent Systems10.1145/367227614:3(1-40)Online publication date: 11-Jun-2024
https://dl.acm.org/doi/10.1145/3672276
Suchmann LKrämer NZiegler JBoratto LGena CMarras MGermanakos PPopescus E(2024)Dynamic Ridge Plot Sliders: Supporting Users' Understanding of the Item Space Structure and Feature Dependencies in Interactive Recommender SystemsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664872(106-113)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664872
Smyth BFeely CBerndsen JCaulfield BLawlor A(2024)Learning to Run Marathons: On the Applications of Machine Learning to Recreational Marathon RunningArtificial Intelligence in Sports, Movement, and Health10.1007/978-3-031-67256-9_13(209-231)Online publication date: 3-Sep-2024
https://doi.org/10.1007/978-3-031-67256-9_13
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Index Terms

PeerChooser: visual interactive recommendation
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

Published In

CHI '08: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

April 2008

1870 pages

ISBN:9781605580111

DOI:10.1145/1357054

General Chairs:
Mary Czerwinski
Microsoft Research, USA
,
Arnie Lund
Microsoft, USA
,
Program Chair:
Desney Tan
Microsoft Research, USA

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: 06 April 2008

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CHI '08

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CHI '08: CHI Conference on Human Factors in Computing Systems

April 5 - 10, 2008

Florence, Italy

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CHI '08 Paper Acceptance Rate 157 of 714 submissions, 22%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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Cited By

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Chatti MGuesmi MMuslim A(2024)Visualization for Recommendation Explainability: A Survey and New PerspectivesACM Transactions on Interactive Intelligent Systems10.1145/367227614:3(1-40)Online publication date: 11-Jun-2024
https://dl.acm.org/doi/10.1145/3672276
Suchmann LKrämer NZiegler JBoratto LGena CMarras MGermanakos PPopescus E(2024)Dynamic Ridge Plot Sliders: Supporting Users' Understanding of the Item Space Structure and Feature Dependencies in Interactive Recommender SystemsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664872(106-113)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664872
Smyth BFeely CBerndsen JCaulfield BLawlor A(2024)Learning to Run Marathons: On the Applications of Machine Learning to Recreational Marathon RunningArtificial Intelligence in Sports, Movement, and Health10.1007/978-3-031-67256-9_13(209-231)Online publication date: 3-Sep-2024
https://doi.org/10.1007/978-3-031-67256-9_13
Guesmi MChatti MJoarder SAin QSiepmann CGhanbarzadeh HAlatrash R(2023)Justification vs. Transparency: Why and How Visual Explanations in a Scientific Literature Recommender SystemInformation10.3390/info1407040114:7(401)Online publication date: 14-Jul-2023
https://doi.org/10.3390/info14070401
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
Ooge JDereu LVerbert K(2023)Steering Recommendations and Visualising Its Impact: Effects on Adolescents’ Trust in E-Learning PlatformsProceedings of the 28th International Conference on Intelligent User Interfaces10.1145/3581641.3584046(156-170)Online publication date: 27-Mar-2023
https://dl.acm.org/doi/10.1145/3581641.3584046
Lu HMa WWang YZhang MWang XLiu YChua TMa S(2023)User Perception of Recommendation Explanation: Are Your Explanations What Users Need?ACM Transactions on Information Systems10.1145/356548041:2(1-31)Online publication date: 25-Jan-2023
https://dl.acm.org/doi/10.1145/3565480
Jafari EVassileva J(2023)Ethical issues in explanations of personalized recommender systemsAdjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3563359.3597383(215-219)Online publication date: 26-Jun-2023
https://dl.acm.org/doi/10.1145/3563359.3597383
Suchmann LKrämer NZiegler J(2023)Branching Preferences: Visualizing Non-linear Topic Progression in Conversational Recommender SystemsAdjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3563359.3597380(199-205)Online publication date: 26-Jun-2023
https://dl.acm.org/doi/10.1145/3563359.3597380
Jeon GKim SLee S(2023)Interactive Feedback Loop with Counterfactual Data Modification for Serendipity in a Recommendation SystemInternational Journal of Human–Computer Interaction10.1080/10447318.2023.223836940:19(5585-5601)Online publication date: 2-Aug-2023
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A Scalable, Accurate Hybrid Recommender System

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