skip to main content
10.1145/3604915.3608751acmconferencesArticle/Chapter ViewAbstractPublication PagesrecsysConference Proceedingsconference-collections
extended-abstract

BehavRec: Workshop on Recommendations for Behavior Change

Published: 14 September 2023 Publication History

Abstract

The workshop aims to discuss open problems, challenges, and innovative research approaches in the area of persuasive and behavior change recommender systems, that is, recommender systems aimed at modifying people's habits and behavior. Some questions that motivate this workshop are: What kind of theory is more suitable to inform the design of behavior change recommender systems? What kind of personal data (e.g., coming from environmental sensors, wearable devices, etc.) should we use to design behavior change recommendations? How should we deliver them (i.e., what kind of communication channels and interfaces should we use)? What kind of strategies should we implement to design timely and contextualized recommendations? How can we support the user's motivation to adhere to the recommendations provided? How can we “persuade” users in the long term?

References

[1]
Cena, F. Rapp, A., Musto, C., Semeraro, G. (2020). Generating recommendations from multiple data sources: A methodological framework for system design and its application. IEEE Access, 8, 183430- 183447.
[2]
Yoon-Min Cho, Seohyun Lee, Sheikh Mohammed Shariful Islam, and Sun-Young Kim. 2018. Theories Applied to m-Health Interventions for Behavior Change in Low- and Middle-Income Countries: A Systematic Review. Telemedicine and e-Health 24, 10 (Oct. 2018), 727–741. https://doi.org/10.1089/tmj.2017.0249 Publisher: Mary Ann Liebert, Inc., publishers.
[3]
David Elsweiler, Bernd Ludwig, Alan Said, Hanna Schaefer, and Christoph Trattner. 2016. Engendering Health with Recommender Systems. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16). Association for Computing Machinery, New York, NY, USA, 409–410. https://doi.org/10.1145/2959100.2959203
[4]
Rodrigo Zenun Franco. 2017. Online Recommender System for Personalized Nutrition Advice. In Proceedings of the Eleventh ACM Conference on Recommender Systems (RecSys '17). Association for Computing Machinery, New York, NY, USA, 411–415. https://doi.org/10.1145/3109859.3109862
[5]
Santiago Hors-Fraile, Octavio Rivera-Romero, Francine Schneider, Luis Fernandez-Luque, Francisco Luna-Perejon, Anton Civit-Balcells, and Hein de Vries. 2018. Analyzing recommender systems for health promotion using a multidisciplinary taxonomy: A scoping review. International Journal of Medical Informatics 114 (June 2018), 143–155. https://doi.org/10.1016/j.ijmedinf.2017.12.018
[6]
Mohammed Khwaja, Miquel Ferrer, Jesus Omana Iglesias, A. Aldo Faisal, and Aleksandar Matic. 2019. Aligning daily activities with personality: towards a recommender system for improving wellbeing. In Proceedings of the 13th ACM Conference on Recommender Systems (RecSys '19). Association for Computing Machinery, New York, NY, USA, 368–372. https://doi.org/10.1145/3298689.3347020
[7]
Yoo, Kyung-Hyan, Ulrike Gretzel, and Markus Zanker. Persuasive recommender systems: conceptual background and implications. Springer Science & Business Media, 2012.
[8]
Mia Liza A. Lustria, Seth M. Noar, Juliann Cortese, Stephanie K. Van Stee, Robert L. Glueckauf, and Junga Lee. 2013. A Meta-Analysis of Web-Delivered Tailored Health Behavior Change Interventions. Journal of Health Communication 18, 9 (Sept. 2013), 1039–1069. https://doi.org/10.1080/10810730.2013.768727
[9]
Cataldo Musto, Alain D. Starke, Christoph Trattner, Amon Rapp, and Giovanni Semeraro. 2021. Exploring the Effects of Natural Language Justifications in Food Recommender Systems. In Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization (UMAP '21). Association for Computing Machinery, New York, NY, USA, 147–157. https://doi.org/10.1145/3450613.3456827
[10]
Inbal Nahum-Shani, Shawna N. Smith, Bonnie J. Spring, Linda M. Collins, Katie Witkiewitz, Ambuj Tewari, and Susan A. Murphy. 2018. Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support. Annals of Behavioral Medicine 52, 6 (May 2018), 446–462. https://doi.org/10.1007/s12160-016-9830-8 Publisher: Oxford Academic
[11]
Amon Rapp and Arianna Boldi. 2023. Exploring the Lived Experience of Behavior Change Technologies: Towards an Existential Model of Behavior Change for HCI. ACM Trans. Comput.-Hum. Interact. https://doi.org/10.1145/3603497
[12]
Amon Rapp, Maurizio Tirassa, and Lia Tirabeni. 2019. Rethinking Technologies for Behavior Change: A View from the Inside of Human Change. ACM Trans. Comput.-Hum. Interact. 26, 4, Article 22 (August 2019), 30 pages. https://doi.org/10.1145/3318142
[13]
William T. Riley, Daniel E. Rivera, Audie A. Atienza, Wendy Nilsen, Susannah M. Allison, and Robin Mermelstein. 2011. Health behavior models in the age of mobile interventions: are our theories up to the task? Translational Behavioral Medicine 1, 1 (March 2011), 53–71. https://doi.org/10.1007/s13142-011-0021-7 Publisher: Oxford Academic.
[14]
Hanna Schäfer, Santiago Hors-Fraile, Raghav Pavan Karumur, André Calero Valdez, Alan Said, Helma Torkamaan, Tom Ulmer, and Christoph Trattner. 2017. Towards Health (Aware) Recommender Systems. In Proceedings of the 2017 International Conference on Digital Health (DH '17). Association for Computing Machinery, New York, NY, USA, 157–161. https://doi.org/10.1145/3079452.3079499
[15]
Helma Torkamaan and Jürgen Ziegler. 2021. Integrating Behavior Change and Persuasive Design Theories into an Example Mobile Health Recommender System. In Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers (UbiComp '21). Association for Computing Machinery, New York, NY, USA, 218–225. https://doi.org/10.1145/3460418.3479330

Cited By

View all
  • (2024)The 6th International Workshop on Health Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3687113(1232-1236)Online publication date: 8-Oct-2024

Index Terms

  1. BehavRec: Workshop on Recommendations for Behavior Change

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems
    September 2023
    1406 pages
    Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 14 September 2023

    Check for updates

    Author Tags

    1. Behavior change
    2. Persuasive technologies
    3. Recommender systems

    Qualifiers

    • Extended-abstract
    • Research
    • Refereed limited

    Conference

    RecSys '23: Seventeenth ACM Conference on Recommender Systems
    September 18 - 22, 2023
    Singapore, Singapore

    Acceptance Rates

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

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)44
    • Downloads (Last 6 weeks)11
    Reflects downloads up to 28 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)The 6th International Workshop on Health Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3687113(1232-1236)Online publication date: 8-Oct-2024

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media