short-paper

Rating-based Preference Elicitation for Recommendation of Stress Intervention

Authors:

Helma Torkamaan,

Jürgen ZieglerAuthors Info & Claims

UMAP '19: Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization

Pages 46 - 50

https://doi.org/10.1145/3320435.3324990

Published: 07 June 2019 Publication History

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Abstract

In recent years, recommender systems have emerged as a key component for personalization in health applications. Central in the development of recommender systems is rating-based preference elicitation, based both on single-criterion and multi-criteria rating. Though its use has already been studied in various domains of recommender systems, far too little attention has been paid to preference elicitation in health recommender systems~(HRS). The purpose of this paper is to develop a better understanding of this preference elicitation by studying the criteria that users consider when they rate a health promotion recommendation from HRS, and accordingly, to offer a design solution as a functional feedback model for mobile health applications. This paper investigates the user-perceived importance of various criteria, as well as latent factors for eliciting user feedback on the recommendations. It also reports the relationship of explanation and trust to the overall rating. By aggregating a list of all possible criteria, we further discover that not all criteria are equally important to users, and that the effectiveness of a recommendation plays a dominant role.

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Cited By

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Torkamaan HZiegler J(2022)Recommendations as Challenges: Estimating Required Effort and User Ability for Health Behavior Change RecommendationsProceedings of the 27th International Conference on Intelligent User Interfaces10.1145/3490099.3511118(106-119)Online publication date: 22-Mar-2022
https://dl.acm.org/doi/10.1145/3490099.3511118
Wang CSahebi STorkamaan H(2021)STRETCH: Stress and Behavior Modeling with Tensor Decomposition of Heterogeneous DataIEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology10.1145/3486622.3493967(453-462)Online publication date: 14-Dec-2021
https://dl.acm.org/doi/10.1145/3486622.3493967
Torkamaan HZiegler J(2021)Integrating Behavior Change and Persuasive Design Theories into an Example Mobile Health Recommender SystemAdjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers10.1145/3460418.3479330(218-225)Online publication date: 21-Sep-2021
https://dl.acm.org/doi/10.1145/3460418.3479330
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Rating-based Preference Elicitation for Recommendation of Stress Intervention

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Published In

UMAP '19: Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization

June 2019

377 pages

ISBN:9781450360210

DOI:10.1145/3320435

General Chairs:
George Angelos Papadopoulos
University of Cyprus, Cyprus
,
George Samaras
University of Cyprus, Cyprus
,
Stephan Weibelzahl
PFH Private University of Applied Sciences Göttingen, Germany
,
Program Chairs:
Dietmar Jannach
Alpen-Adria-Universität Klagenfurt, Austria
,
Olga C. Santos
aDeNu Research Group - UNED, Spain

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 the author(s) 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: 07 June 2019

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UMAP '19

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UMAP '19: 27th Conference on User Modeling, Adaptation and Personalization

June 9 - 12, 2019

Larnaca, Cyprus

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UMAP '19 Paper Acceptance Rate 30 of 122 submissions, 25%;

Overall Acceptance Rate 162 of 633 submissions, 26%

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Cited By

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Torkamaan HZiegler J(2022)Recommendations as Challenges: Estimating Required Effort and User Ability for Health Behavior Change RecommendationsProceedings of the 27th International Conference on Intelligent User Interfaces10.1145/3490099.3511118(106-119)Online publication date: 22-Mar-2022
https://dl.acm.org/doi/10.1145/3490099.3511118
Wang CSahebi STorkamaan H(2021)STRETCH: Stress and Behavior Modeling with Tensor Decomposition of Heterogeneous DataIEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology10.1145/3486622.3493967(453-462)Online publication date: 14-Dec-2021
https://dl.acm.org/doi/10.1145/3486622.3493967
Torkamaan HZiegler J(2021)Integrating Behavior Change and Persuasive Design Theories into an Example Mobile Health Recommender SystemAdjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers10.1145/3460418.3479330(218-225)Online publication date: 21-Sep-2021
https://dl.acm.org/doi/10.1145/3460418.3479330
Oyebode ONdulue CAdib AMulchandani DSuruliraj BOrji FChambers CMeier SOrji R(2020)Health, Psychosocial, and Social issues emanating from COVID-19 pandemic based on Social Media Comments using Text Mining and Thematic Analysis (Preprint)JMIR Medical Informatics10.2196/22734Online publication date: 21-Jul-2020
https://doi.org/10.2196/22734
Mejova YSuarez-Lledó V(2020)Impact of Online Health Awareness Campaign: Case of National Eating Disorders AssociationSocial Informatics10.1007/978-3-030-60975-7_15(192-205)Online publication date: 7-Oct-2020
https://doi.org/10.1007/978-3-030-60975-7_15
Hors-Fraile Sde Vries HMalwade SLuna-Perejon FAmaya CCivit ASchneider FBamidis PSyed-Abdul SLi Y(2019)Opening the Black Box: Explaining the Process of Basing a Health Recommender System on the I-Change Behavioral Change ModelIEEE Access10.1109/ACCESS.2019.29576967(176525-176540)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2957696

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Semi-supervised discriminative preference elicitation for cold-start recommendation

A comparative user study on rating vs. personality quiz based preference elicitation methods

New Recommendation Techniques for Multicriteria Rating Systems