%0 Conference Paper %1 El_Majjodi_2022 %A Majjodi, Ayoub El %A Starke, Alain D. %A Trattner, Christoph %B Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization %D 2022 %I ACM %K food-recommender health umap2022 %P 48-56 %R 10.1145/3503252.3531312 %T Nudging Towards Health? Examining the Merits of Nutrition Labels and Personalization in a Recipe Recommender System %U https://doi.org/10.1145%2F3503252.3531312 %X 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.

@inproceedings{El_Majjodi_2022, 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. }, added-at = {2022-07-04T16:35:23.000+0200}, author = {Majjodi, Ayoub El and Starke, Alain D. and Trattner, Christoph}, biburl = {https://www.bibsonomy.org/bibtex/245d49fe499bd7835531f1755816a67ec/brusilovsky}, booktitle = {Proceedings of the 30th {ACM} Conference on User Modeling, Adaptation and Personalization}, description = {Nudging Towards Health? Examining the Merits of Nutrition Labels and Personalization in a Recipe Recommender System | Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization}, doi = {10.1145/3503252.3531312}, interhash = {d05559dc96a4880aa7152c826ea760cc}, intrahash = {45d49fe499bd7835531f1755816a67ec}, keywords = {food-recommender health umap2022}, month = jul, note = {Controversial results on decreasing value of recommendation...}, pages = {48-56}, publisher = {{ACM}}, timestamp = {2022-07-04T16:35:23.000+0200}, title = {Nudging Towards Health? Examining the Merits of Nutrition Labels and Personalization in a Recipe Recommender System}, url = {https://doi.org/10.1145%2F3503252.3531312}, year = 2022 }