@inproceedings{b84ccef2752d49a38fb30d9b6e9bb009,
title = "The cholesterol factor: Balancing accuracy and health in recipe recommendation through a nutrient-specific metric",
abstract = "Whereas many food recommender systems optimize for users' preferences, health is another but often overlooked objective. This paper aims to recommend relevant recipes that avoid nutrients that contribute to high levels of cholesterol, such as saturated fat and sugar. We introduce a novel metric called 'The Cholesterol Factor', based on nutritional guidelines from the Norwegian Directorate of Health, that can balance accuracy and health through linear re-weighting in post-filtering. We tested popular recommender approaches by evaluating a recipe dataset from AllRecipes.com, in which a CF-based SVD method outperformed content-based and hybrid methods. Although we found that increasing the healthiness of a recommended recipe set came at the cost of Precision and Recall metrics, only putting little weight (10-15%) on our Cholesterol Factor can significantly improve the healthiness of a recommendation set with minimal accuracy losses.",
keywords = "Health, Nutrients, Offline evaluation, Recipes, Recommender systems",
author = "Alain Starke and Christoph Trattner and Hedda Bakken and Martin Johannessen and Vegard Solberg",
year = "2021",
month = sep,
language = "English",
volume = "2959",
series = "CEUR Workshop Proceedings",
publisher = "CEUR Workshop Proceedings (CEUR-WS.org)",
editor = "H. Abdollahpouri and M. Elahi and M. Mansoury and S. Sahebi and Z. Nazari and A. Chaney and B. Loni",
booktitle = "Proceedings of the 1st Workshop on Multi-Objective Recommender Systems (MORS 2021)",
note = "1st Workshop on Multi-Objective Recommender Systems, MORS 2021 ; Conference date: 25-09-2021",
}