research-article

The relation between user intervention and user satisfaction for information recommendation

Authors:

Yoshinori Hijikata,

Shogo NishidaAuthors Info & Claims

SAC '12: Proceedings of the 27th Annual ACM Symposium on Applied Computing

Pages 2002 - 2007

https://doi.org/10.1145/2245276.2232109

Published: 26 March 2012 Publication History

Abstract

Although recommender systems have come to give recommendations with high precision, users are not always satisfied with the recommendations. User satisfaction is apparently influenced by many other factors. We specifically examined user intervention as one factor influencing user satisfaction. We tested two hypotheses: user intervention itself improves user satisfaction; and the more users intervene in the recommendation process, the more they are satisfied with the recommendations. We conducted an experiment incorporating user intervention of several kinds to reveal the relation between user intervention and user satisfaction.

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Cited By

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
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
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
Show More Cited By

Index Terms

The relation between user intervention and user satisfaction for information recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

cover image ACM Conferences

SAC '12: Proceedings of the 27th Annual ACM Symposium on Applied Computing

March 2012

2179 pages

ISBN:9781450308571

DOI:10.1145/2245276

Conference Chairs:
Sascha Ossowski
University Rey Juan Carlos, Spain
,
Paola Lecca
The Microsoft Research - University of Trento COSBI, Italy

Copyright © 2012 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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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 March 2012

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SAC 2012

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SIGAPP

SAC 2012: ACM Symposium on Applied Computing

March 26 - 30, 2012

Trento, Italy

Acceptance Rates

SAC '12 Paper Acceptance Rate 270 of 1,056 submissions, 26%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

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
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
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
Park JKim MKim H(2023)Image Is All for Music Retrieval: Interactive Music Retrieval System Using Images with Mood and Theme AttributesInternational Journal of Human–Computer Interaction10.1080/10447318.2023.220155740:14(3841-3855)Online publication date: 24-Apr-2023
https://doi.org/10.1080/10447318.2023.2201557
Xu MChang QMiyazaki J(2023)How Does the System Perceive Me? — A Transparent and Tunable Recommender SystemDatabase and Expert Systems Applications10.1007/978-3-031-39821-6_3(33-48)Online publication date: 16-Aug-2023
https://doi.org/10.1007/978-3-031-39821-6_3
Wang WFeng FNie LChua TAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)User-controllable Recommendation Against Filter BubblesProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3532075(1251-1261)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3532075
Radlinski FBalog KDiaz FDixon LWedin BAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)On Natural Language User Profiles for Transparent and Scrutable RecommendationProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531873(2863-2874)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531873
Calero Valdez A(2020)Human and Algorithmic Contributions to Misinformation Online - Identifying the CulpritDisinformation in Open Online Media10.1007/978-3-030-39627-5_1(3-15)Online publication date: 29-Jan-2020
https://doi.org/10.1007/978-3-030-39627-5_1
Millecamp MHtun NJin YVerbert KMitrovic TZhang JChen LChin D(2018)Controlling Spotify RecommendationsProceedings of the 26th Conference on User Modeling, Adaptation and Personalization10.1145/3209219.3209223(101-109)Online publication date: 3-Jul-2018
https://dl.acm.org/doi/10.1145/3209219.3209223
Jugovac MJannach D(2017)Interacting with Recommenders—Overview and Research DirectionsACM Transactions on Interactive Intelligent Systems10.1145/30018377:3(1-46)Online publication date: 19-Sep-2017
https://dl.acm.org/doi/10.1145/3001837
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