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Nudging Towards Health? Examining the Merits of Nutrition Labels and Personalization in a Recipe Recommender System

Authors:

Ayoub El Majjodi,

Alain D. Starke, and

Christoph TrattnerAuthors Info & Claims

UMAP '22: Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization

July 2022

Pages 48 - 56

https://doi.org/10.1145/3503252.3531312

Published: 04 July 2022 Publication History

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Abstract

Food recommender systems show personalized recipes to users based on content liked previously. Despite their potential, often recommended (popular) recipes in previous studies have turned out to be unhealthy, negatively contributing to prevalent obesity problems worldwide. Changing how foods are presented through digital nudges might help, but these are usually examined in non-personalized contexts, such as a brick-and-mortar supermarket. This study seeks to support healthy food choices in a personalized interface by adding front-of-package nutrition labels to recipes in a food recommender system. After performing an offline evaluation, we conducted an online study (N = 600) with six different recommender interfaces, based on a 2 (non-personalized vs. personalized recipe advice) x 3 (No Label, Multiple Traffic Light, Nutri-Score) between-subjects design. We found that recipe choices made in the non-personalized scenario were healthier, while the use of nutrition labels (our digital nudge) reduced choice difficulty when the content was personalized.

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MP4 File (UAMP22-18.mp4)

Food recommender systems show personalized recipes to users based on content liked previously. Despite their potential, often recommended (popular) recipes in previous studies have turned out to be unhealthy, negatively contributing to prevalent obesity problems worldwide. Changing how foods are presented through digital nudges might help, but these are usually examined in non-personalized contexts, such as a brick-and-mortar supermarket. This study seeks to support healthy food choices in a personalized interface by adding front-of-package nutrition labels to recipes in a food recommender system. After performing an offline evaluation, we conducted an online study (N = 600) with six different recommender interfaces, based on a 2 (non-personalized vs. personalized recipe advice) x 3 (No Label, Multiple Traffic Light, Nutri-Score) between-subjects design. We found that recipe choices made in the non-personalized scenario were healthier, while the use of nutrition labels (our digital nudge) reduced choice difficulty when the content was personalized.

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Index Terms

Nudging Towards Health? Examining the Merits of Nutrition Labels and Personalization in a Recipe Recommender System

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

UMAP '22: Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization

July 2022

360 pages

ISBN:9781450392075

DOI:10.1145/3503252

General Chairs:
Alejandro Bellogin
Universidad Autonoma de Madrid, Spain
,
Ludovico Boratto
University of Cagliari, Italy
,
Olga C. Santos
UNED, Spain
,
Program Chairs:
Liliana Ardissono
Universita di Torino, Italy
,
Bart Knijnenburg
Clemson University, United States

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Published: 04 July 2022

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UMAP '22: 30th ACM Conference on User Modeling, Adaptation and Personalization

July 4 - 7, 2022

Barcelona, Spain

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Cited By

Hendrawan RBrusilovsky PLekshmi Narayanan ABarria-Pineda J(2024)Explanations in Open User Models for Personalized Information ExplorationAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3665188(256-263)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3665188
Geninatti Cossatin A(2024)Promoting Green Fashion Consumption in Recommender SystemsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664922(50-54)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664922
Echterhoff JMelkote AKancherla SMcAuley J(2024)Avoiding Decision Fatigue with AI-Assisted Decision-MakingProceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3627043.3659569(1-11)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3627043.3659569
Cossatin AMauro NArdissono L(2024)Promoting Green Fashion Consumption Through Digital Nudges in Recommender SystemsIEEE Access10.1109/ACCESS.2024.334971012(6812-6829)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3349710
Felfernig AWundara MTran TPolat-Erdeniz SLubos SEl Mansi MGarber DLe V(2023)Recommender systems for sustainability: overview and research issuesFrontiers in Big Data10.3389/fdata.2023.12845116Online publication date: 30-Oct-2023
https://doi.org/10.3389/fdata.2023.1284511
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