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User Nutrition Modelling and Recommendation - Balancing Simplicity and Complexity
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2017 (English)Conference 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.
National Category
Other Computer and Information Science
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-13579OAI: oai:DiVA.org:his-13579DiVA: diva2:1097952
Conference
ACM UMAP, 9-12th July, 2017 at FIIT STU, Bratislava, Slovakia
Available from: 2017-05-23 Created: 2017-05-23 Last updated: 2017-05-23

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf