User Nutrition Modelling and Recommendation - Balancing Simplicity and ComplexityShow others and affiliations
2017 (English)In: UMAP '17 Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization / [ed] Marko Tkalcic; Dhaval Thakker; Panagiotis Germanakos; Kalina Yacef; Cecile Paris; Olga Santos, Association for Computing Machinery (ACM), 2017, p. 93-96Conference paper, Published paper (Refereed)
Abstract [en]
In order to use and model nutritional knowledge in a food recommender system, uncertainties regarding the users nutritional state and thus the personal health value of food items, as well as conflicting nutritional theories need to be quantified, qualified and subsumed into falsifiable models. In this paper, we reflect on different error sources with respect to nutrition and consider how such issues can be tackled in future systems. We discuss the integration of general nutritional theories into information systems as well as user specific nutritional measures and different approaches to evaluating the utility of a given nutritional model.
Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2017. p. 93-96
National Category
Other Computer and Information Science
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
URN: urn:nbn:se:his:diva-13579DOI: 10.1145/3099023.3099108ISI: 000850443800016Scopus ID: 2-s2.0-85026867002ISBN: 978-1-4503-5067-9 (print)OAI: oai:DiVA.org:his-13579DiVA, id: diva2:1097952
Conference
25th Conference on User Modeling, Adaptation and Personalization, ACM UMAP, 9-12th July, 2017 at FIIT STU, Bratislava, Slovakia
2017-05-232017-05-232024-05-20Bibliographically approved