@inproceedings{3e867a1444884efa8ca7a02689a60231,
title = "Recipe recommendation: accuracy and reasoning",
abstract = "Food and diet are complex domains for recommender technology, but the need for systems that assist users in embarking on and engaging with healthy living programs has never been more real. One key to sustaining long term engagement with eHealth services is the provision of tools, which assist and train users in planning correctly around the areas of diet and exercise. These tools require an understanding of user reasoning as well as user needs and are ideal application areas for recommender and personalization technologies. Here, we report on a large scale analysis of real user ratings on a set of recipes in order to judge the applicability and practicality of a number of personalization algorithms. Further to this, we report on apparent user reasoning patterns uncovered in rating data supplied for recipes and suggest ways to exploit this reasoning understanding in the recommendation process.",
keywords = "collaborative filtering, content-based, machine learning, recipes, personalization",
author = "Jill Freyne and Shlomo Berkovsky and Gregory Smith",
year = "2011",
doi = "10.1007/978-3-642-22362-4\_9",
language = "English",
isbn = "9783642223617",
series = "Lecture Notes in Computer Science",
publisher = "Springer, Springer Nature",
pages = "99--110",
editor = "Konstan, \{Joseph A.\} and Ricardo Conejo and Marzo, \{Jos{\'e} L.\} and Nuria Oliver",
booktitle = "User modeling, adaptation, and personalization",
address = "United States",
note = "User Modeling, Adaptation and Personalization Conference, UMAP 2011 ; Conference date: 11-07-2011 Through 15-07-2011",
}