Ayoub El Majjodi
PhD Candidate
University of Bergen
Ayoub El Majjodi is a PhD student at the DARS research group. After he obtained his MSc in Data Science and Big Data in 2019, he worked as a research assistant at Tempere University, Finland. The main focus of his research project lies within Recommender Systems for the food domain, where he will investigate the utility of Recommender systems and Digital nudging to change people's eating behaviors and help them to attain healthier eating habits.
2025
Majjodi, Ayoub El; Starke, Alain D.; Petruzzelli, Alessandro; Musto, Cataldo
2025.
@workshop{nudging25,
title = {Nudging Healthy Choices: Leveraging LLM-Generated Hashtags and Explanations in Personalized Food Recommendations},
author = {Ayoub El Majjodi and Alain D. Starke and Alessandro Petruzzelli and Cataldo Musto},
url = {https://mediafutures.no/llms/},
year = {2025},
date = {2025-09-26},
urldate = {2025-09-26},
issue = {IntRS’25},
abstract = {Making healthy recipe choices can be challenging for users, requiring time and knowledge to differentiate among
various options. These choices are often generated by personalized recommender systems that account for
individual preferences. One effective approach to encouraging healthier food choices is to intervene in how
these choices are presented to users. In this paper, we explore the impact of nutritional food labels and evaluate
the effectiveness of a Large Language Model (LLM) in generating high-quality explanations and hashtags to
support users in making healthier food decisions. In an online experiment (N = 240), we designed a knowledge-
based recommender system to generate personalized recipes for each user. Recipes were annotated with one of
four intervention, a Multiple Traffic Light (MTL) nutrition label, LLM-generated explanations, LLM-generated
hashtags, or no label (baseline). Our findings indicate that the interventions significantly enhanced users’ ability
to select healthier recipes. Additionally, we examined how different system components affected the overall user
experience and how these components interacted with one another},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
Making healthy recipe choices can be challenging for users, requiring time and knowledge to differentiate among
various options. These choices are often generated by personalized recommender systems that account for
individual preferences. One effective approach to encouraging healthier food choices is to intervene in how
these choices are presented to users. In this paper, we explore the impact of nutritional food labels and evaluate
the effectiveness of a Large Language Model (LLM) in generating high-quality explanations and hashtags to
support users in making healthier food decisions. In an online experiment (N = 240), we designed a knowledge-
based recommender system to generate personalized recipes for each user. Recipes were annotated with one of
four intervention, a Multiple Traffic Light (MTL) nutrition label, LLM-generated explanations, LLM-generated
hashtags, or no label (baseline). Our findings indicate that the interventions significantly enhanced users’ ability
to select healthier recipes. Additionally, we examined how different system components affected the overall user
experience and how these components interacted with one another
various options. These choices are often generated by personalized recommender systems that account for
individual preferences. One effective approach to encouraging healthier food choices is to intervene in how
these choices are presented to users. In this paper, we explore the impact of nutritional food labels and evaluate
the effectiveness of a Large Language Model (LLM) in generating high-quality explanations and hashtags to
support users in making healthier food decisions. In an online experiment (N = 240), we designed a knowledge-
based recommender system to generate personalized recipes for each user. Recipes were annotated with one of
four intervention, a Multiple Traffic Light (MTL) nutrition label, LLM-generated explanations, LLM-generated
hashtags, or no label (baseline). Our findings indicate that the interventions significantly enhanced users’ ability
to select healthier recipes. Additionally, we examined how different system components affected the overall user
experience and how these components interacted with one another
Majjodi, Ayoub El; Starke, Alain D.; Trattner, Christoph
Integrating Digital Food Nudges and Recommender Systems: Current Status and Future Directions Journal Article
In: IEEE Access, 2025.
@article{integratingdigital25,
title = {Integrating Digital Food Nudges and Recommender Systems: Current Status and Future Directions},
author = {Ayoub El Majjodi and Alain D. Starke and Christoph Trattner},
url = {https://mediafutures.no/integrating_digital_food_nudges_and_recommender_systems_current_status_and_future_directions/},
year = {2025},
date = {2025-07-14},
journal = {IEEE Access},
abstract = {Recommender systems are widely regarded as effective tools for facilitating the discovery of relevant content. In the food domain, they help users find recipes, choose grocery products, and generate meal suggestions. While they address the challenge of choice overload, their direct influence on promoting healthier food choices remains limited. Digital nudges could further assist in guiding users toward healthier decisions, enhancing the accessibility and visibility of healthy options when integrated into a recommender system. This review examines to what extent food recommender systems have so far successfully incorporated digital nudges for healthy food promotion and which challenges still remain. We present a classification and analysis of various digital nudging strategies employed for this purpose, as well as opportunities for future research. We emphasize that various nudging techniques have the potential to support users in making healthier food choices within food recommender systems. Furthermore, user-centric evaluations represent the most effective approach for assessing the performance of these systems.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Recommender systems are widely regarded as effective tools for facilitating the discovery of relevant content. In the food domain, they help users find recipes, choose grocery products, and generate meal suggestions. While they address the challenge of choice overload, their direct influence on promoting healthier food choices remains limited. Digital nudges could further assist in guiding users toward healthier decisions, enhancing the accessibility and visibility of healthy options when integrated into a recommender system. This review examines to what extent food recommender systems have so far successfully incorporated digital nudges for healthy food promotion and which challenges still remain. We present a classification and analysis of various digital nudging strategies employed for this purpose, as well as opportunities for future research. We emphasize that various nudging techniques have the potential to support users in making healthier food choices within food recommender systems. Furthermore, user-centric evaluations represent the most effective approach for assessing the performance of these systems.
2024
Majjodi, Ayoub El; Khan, Sohail Ahmed; Starke, Alain D.; Elahi, Mehdi; Trattner, Christoph
Advancing Visual Food Attractiveness Predictions for Healthy Food Recommender Systems Conference
The ACM Conference on Recommender Systems (RecSys) 2024, 2024.
@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}
}
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.
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.
2023
Majjodi, Ayoub El; Starke, Alain D.; Trattner, Christoph
Association for Computing Machinery (ACM) RecSys ’23, 2023.
@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}
}
2022
Majjodi, Ayoub El; Starke, Alain D.; Trattner, Christoph
Nudging Towards Health? Examining the Merits of Nutrition Labels and Personalization in a Recipe Recommender System, 2022.
@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}
}
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.