research-article

User Nutrition Modelling and Recommendation: Balancing Simplicity and Complexity

Authors:

Hanna Schäfer,

David Elsweiler,

Francesco Ricci,

Alan SaidAuthors Info & Claims

UMAP '17: Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization

Pages 93 - 96

https://doi.org/10.1145/3099023.3099108

Published: 09 July 2017 Publication History

Abstract

In order to use and model nutritional knowledge in a food recommender system, uncertainties regarding the users nutritional state and thus the personal health value of food items, as well as conflicting nutritional theories need to be quantified, qualified and subsumed into falsifiable models. In this paper, we reflect on different error sources with respect to nutrition and consider how such issues can be tackled in future systems. We discuss the integration of general nutritional theories into information systems as well as user specific nutritional measures and different approaches to evaluating the utility of a given nutritional model.

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Cited By

Forouzandeh SRostami MBerahmand KSheikhpour R(2024)Health-aware food recommendation system with dual attention in heterogeneous graphsComputers in Biology and Medicine10.1016/j.compbiomed.2023.107882169(107882)Online publication date: Feb-2024
https://doi.org/10.1016/j.compbiomed.2023.107882
Jia NChen JWang RLi M(2024)Towards automatically generating meal plan based on genetic algorithmSoft Computing10.1007/s00500-023-09556-028:9-10(6893-6908)Online publication date: 11-Jan-2024
https://doi.org/10.1007/s00500-023-09556-0
Zhang JWang ZLiu WLiu XZheng Q(2023)A unified approach to designing sequence-based personalized food recommendation systems: tackling dynamic user behaviorsInternational Journal of Machine Learning and Cybernetics10.1007/s13042-023-01808-714:9(2903-2912)Online publication date: 25-Mar-2023
https://doi.org/10.1007/s13042-023-01808-7
Show More Cited By

Index Terms

User Nutrition Modelling and Recommendation: Balancing Simplicity and Complexity
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
2. Information systems
  1. Information systems applications
    1. Decision support systems

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Published In

cover image ACM Conferences

UMAP '17: Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization

July 2017

456 pages

ISBN:9781450350679

DOI:10.1145/3099023

Editors:
Marko Tkalcic
Free University of Bolzano
,
Dhaval Thakker
University of Leeds
,
Panagiotis Germanakos
SAP SE & University of Cyprus
,
Kalina Yacef
University of Sydney
,
Cecile Paris
CSIRO ICT Centre
,
Olga Santos
Spanish National University for Distance Education
,
General Chairs:
Maria Bielikova
Slovak University of Technology in Bratislava, Slovakia
,
Eelco Herder
L3S Research Center, Germany and Radboud Universiteit Nijmegen, The Netherlands
,
Program Chairs:
Michel Desmarais
Polytechnique University, Montreal, Canada
,
Federica Cena
University of Torino, Italy

Copyright © 2017 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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Published: 09 July 2017

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UMAP '17: 25th Conference on User Modeling, Adaptation and Personalization

July 9 - 12, 2017

Bratislava, Slovakia

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Overall Acceptance Rate 162 of 633 submissions, 26%

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33rd ACM Conference on User Modeling, Adaptation and Personalization

June 16 - 19, 2025

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Cited By

Forouzandeh SRostami MBerahmand KSheikhpour R(2024)Health-aware food recommendation system with dual attention in heterogeneous graphsComputers in Biology and Medicine10.1016/j.compbiomed.2023.107882169(107882)Online publication date: Feb-2024
https://doi.org/10.1016/j.compbiomed.2023.107882
Jia NChen JWang RLi M(2024)Towards automatically generating meal plan based on genetic algorithmSoft Computing10.1007/s00500-023-09556-028:9-10(6893-6908)Online publication date: 11-Jan-2024
https://doi.org/10.1007/s00500-023-09556-0
Zhang JWang ZLiu WLiu XZheng Q(2023)A unified approach to designing sequence-based personalized food recommendation systems: tackling dynamic user behaviorsInternational Journal of Machine Learning and Cybernetics10.1007/s13042-023-01808-714:9(2903-2912)Online publication date: 25-Mar-2023
https://doi.org/10.1007/s13042-023-01808-7
Sheng GSun SLiu CYang Y(2022)Food recognition via an efficient neural network with transformer groupingInternational Journal of Intelligent Systems10.1002/int.2305037:12(11465-11481)Online publication date: 2-Sep-2022
https://doi.org/10.1002/int.23050
Hauptmann HLeipold NMadenach MWintergerst MLurz MGroh GBöhm MGedrich KKrcmar H(2021)Effects and challenges of using a nutrition assistance system: results of a long-term mixed-method studyUser Modeling and User-Adapted Interaction10.1007/s11257-021-09301-y32:5(923-975)Online publication date: 15-Oct-2021
https://doi.org/10.1007/s11257-021-09301-y
Iwendi CKhan SAnajemba JBashir ANoor F(2020)Realizing an Efficient IoMT-Assisted Patient Diet Recommendation System Through Machine Learning ModelIEEE Access10.1109/ACCESS.2020.29685378(28462-28474)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2968537
Nolting MWittke UMayora OForti SMeyer JMamykina L(2019)OJOProceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare10.1145/3329189.3329225(340-345)Online publication date: 20-May-2019
https://dl.acm.org/doi/10.1145/3329189.3329225
Min WJiang SLiu LRui YJain R(2019)A Survey on Food ComputingACM Computing Surveys10.1145/332916852:5(1-36)Online publication date: 13-Sep-2019
https://dl.acm.org/doi/10.1145/3329168
Schäfer HWillemsen MFu WPan SBrdiczka OChau PCalvary G(2019)Rasch-based tailored goals for nutrition assistance systemsProceedings of the 24th International Conference on Intelligent User Interfaces10.1145/3301275.3302298(18-29)Online publication date: 17-Mar-2019
https://dl.acm.org/doi/10.1145/3301275.3302298
Elsweiler DHauptmann HTrattner C(2012)Food Recommender SystemsRecommender Systems Handbook10.1007/978-1-0716-2197-4_23(871-925)Online publication date: 24-Feb-2012
https://doi.org/10.1007/978-1-0716-2197-4_23

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