@inproceedings{eae688a583b543038c7796c79bce4163,
title = "Recommending food: Reasoning on recipes and ingredients",
abstract = "With the number of people considered to be obese rising across the globe, the role of IT solutions in health management has been receiving increased attention by medical professionals in recent years. This paper focuses on an initial step toward understanding the applicability of recommender techniques in the food and diet domain. By understanding the food preferences and assisting users to plan a healthy and appealing meal, we aim to reduce the effort required of users to change their diet. As an initial feasibility study, we evaluate the performance of collaborative filtering, content-based and hybrid recommender algorithms on a dataset of 43,000 ratings from 512 users. We report on the accuracy and coverage of the algorithms and show that a content-based approach with a simple mechanism that breaks down recipe ratings into ingredient ratings performs best overall.",
keywords = "collaborative filtering, content-based, ingredient, recipes",
author = "Jill Freyne and Shlomo Berkovsky",
year = "2010",
doi = "10.1007/978-3-642-13470-8_36",
language = "English",
isbn = "9783642134692",
series = "Lecture Notes in Computer Science",
publisher = "Springer, Springer Nature",
pages = "381--386",
editor = "{De Bra}, Paul and Alfred Kobsa and David Chin",
booktitle = "User Modeling, Adaptation, and Personalization",
address = "United States",
note = "18th International Conference on User Modeling, Adaptation, and Personalization, UMAP 2010 ; Conference date: 20-06-2010 Through 24-06-2010",
}