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Online Recommender System for Personalized Nutrition Advice

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Rodrigo Zenun FrancoAuthors Info & Claims

RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

Pages 411 - 415

https://doi.org/10.1145/3109859.3109862

Published: 27 August 2017 Publication History

Abstract

The general recommendations for addressing non-communicable diseases, which are responsible for two thirds of deaths globally, are mainly related to lifestyle changes, such as diet and physical activity. Challenges with encouraging healthy diets include gathering accurate information about dietary intake and delivering interventions that can influence behavior. Internet technologies offer excellent potential for addressing these challenges. Furthermore, personalized recommendations are more effective than general population-based recommendations at modifying health-related behavior in nutrition interventions.

The overall aim of this project is to design, develop and evaluate a recommender system able to assess dietary intake, using a validated Food Frequency Questionnaire (FFQ), and propose valid personalized nutrition advice for adults. It is investigating an effective way of providing personalized online dietary recommendations to increase diet quality at population-level and of considering an individual user's preferences, population data and experts' knowledge in the recommendation. The system is envisaged to be a web-based service, built with commercially available technologies, scalable, replicable, inexpensive and independent of any bespoke device (e.g. proprietary activity trackers).

Different levels of personalized advice will be evaluated via an online Randomised Control Trial (RCT) and surveys with nutrition professionals will be used for rating the advice proposed by the recommender system.

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Index Terms

Online Recommender System for Personalized Nutrition Advice
1. Applied computing
  1. Life and medical sciences
    1. Health informatics
2. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Expert systems

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Published In

cover image ACM Conferences

RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

August 2017

466 pages

ISBN:9781450346528

DOI:10.1145/3109859

General Chairs:
Paolo Cremonesi
Politecnico di Milano, Italy
,
Francesco Ricci
Free University Bozen-Bolzano, Italy
,
Program Chairs:
Shlomo Berkovsky
CSIRO, Australia
,
Alexander Tuzhilin
New York University, USA

Copyright © 2017 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 August 2017

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Funding Sources

CNPq (National Counsel of Technological and Scientific Development) - Brazil

Conference

RecSys '17

Sponsor:

SIGCHI

RecSys '17: Eleventh ACM Conference on Recommender Systems

August 27 - 31, 2017

Como, Italy

Acceptance Rates

RecSys '17 Paper Acceptance Rate 26 of 125 submissions, 21%;

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

Upcoming Conference

RecSys '24

Sponsor:
sigchi

18th ACM Conference on Recommender Systems

October 14 - 18, 2024

Bari , Italy

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https://doi.org/10.1109/I2CT61223.2024.10544017
Nahar KBanikhalaf MIbrahim FAbual-Rub MAlmomani AGupta B(2023)A Rule-Based Expert Advisory System for Restaurants Using Machine Learning and Knowledge-Based Systems TechniquesInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.33306419:1(1-25)Online publication date: 7-Nov-2023
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Coppens IMartens LDe Pessemier T(2023)Analyzing Accuracy versus Diversity in a Health Recommender System for Physical Activities: a Longitudinal User StudyProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3610650(1146-1151)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3610650
Rapp ACena FTrattner COrji RVassileva JStarke A(2023)BehavRec: Workshop on Recommendations for Behavior ChangeProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608751(1231-1233)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608751
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Kaiser BStelzl TFinglas PGedrich K(2022)The Assessment of a Personalized Nutrition Tool (eNutri) in Germany: Pilot Study on Usability Metrics and Users’ ExperiencesJMIR Formative Research10.2196/344976:8(e34497)Online publication date: 4-Aug-2022
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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
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