Ayoub El Majjodi

@conference{visualfood24,

title = {Advancing Visual Food Attractiveness Predictions for Healthy Food Recommender Systems},

author = {Ayoub El Majjodi and Sohail Ahmed Khan and Alain D. Starke and Mehdi Elahi and Christoph Trattner},

url = {https://mediafutures.no/healthrecsys-2024-ayoub-1/},

year  = {2024},

date = {2024-09-17},

booktitle = {The ACM Conference on Recommender Systems (RecSys) 2024},

abstract = {The visual representation of food has a significant influence on

how people choose food in the real world but also in a digital food

recommender scenario. Previous studies on that matter show that

small change in visual features can change human decision-making,

regardless of whether the food is healthy or not. This paper reports

on a study that aims to understand further how users perceive

the attractiveness of food images in the digital world. In an online

mixed-methods survey (N=192), users provided visual attractive-

ness ratings on a 7-point scale and provided textual assessments

of the visual attractiveness of food images. We found a robust

correlation between fundamental visual features (e.g., contrast, col-

orfulness) and perceived image attractiveness. The analysis also

revealed that cooking skills predicted food image attractiveness

among user factors. Regarding food image dimensions, appearance

and perceived healthiness emerged to be significantly correlated

with user ratings for food image attractiveness.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{inra2023,

title = {The Interplay between Food Knowledge, Nudges, and Preference Elicitation Methods Determines the Evaluation of a Recipe Recommender System},

author = {Ayoub El Majjodi and Alain D. Starke and Christoph Trattner },

url = {https://mediafutures.no/intrs2023-2/},

year  = {2023},

date = {2023-09-18},

urldate = {2023-09-18},

booktitle = {Association for Computing Machinery (ACM) RecSys ’23},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Majjodi2022,

title = {Nudging Towards Health? Examining the Merits of Nutrition Labels and Personalization in a Recipe Recommender System},

author = { Ayoub El Majjodi and Alain D. Starke and Christoph Trattner



 },

url = {https://dl.acm.org/doi/10.1145/3503252.3531312?fbclid=IwAR0eb6MPuISpVs9Vfkd-ww_KN7EjbMbiGdDQnPxjayogfKbHFgkSgeLdaxs},

year  = {2022},

date = {2022-07-03},

urldate = {2022-07-03},

booktitle = {Nudging Towards Health? Examining the Merits of Nutrition Labels and Personalization in a Recipe Recommender System},

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.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}