User Nutrition Modelling and Recommendation

Hanna Schäfer; Mehdi Elahi; David Elsweiler; Georg Groh; Morgan Harvey; Bernd Ludwig; Francesco Ricci; Alan Said

doi:10.1145/3099023.3099108

User Nutrition Modelling and Recommendation

Hanna Schäfer, Mehdi Elahi, David Elsweiler, Georg Groh, Morgan Harvey, Bernd Ludwig, Francesco Ricci, Alan Said

Computer Information Sciences Department

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

15 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization - UMAP '17
Editors	Marko Tkalcic, Dhaval Thakker, Panagiotis Germanakos, Kalina Yacef, Cecile Paris, Olgo Santos
Place of Publication	New York
Publisher	ACM
Pages	93-96
ISBN (Print)	9781450350679
DOIs	https://doi.org/10.1145/3099023.3099108
Publication status	Published - 9 Jul 2017
Event	UMAP '17: 25th Conference on User Modeling, Adaptation and Personalization - Bratislava, Slovakia Duration: 9 Jul 2017 → …

Conference

Conference	UMAP '17: 25th Conference on User Modeling, Adaptation and Personalization
Period	9/07/17 → …

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10.1145/3099023.3099108

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Schäfer, H., Elahi, M., Elsweiler, D., Groh, G., Harvey, M., Ludwig, B., Ricci, F., & Said, A. (2017). User Nutrition Modelling and Recommendation. In M. Tkalcic, D. Thakker, P. Germanakos, K. Yacef, C. Paris, & O. Santos (Eds.), Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization - UMAP '17 (pp. 93-96). ACM. https://doi.org/10.1145/3099023.3099108

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abstract = "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.",

author = "Hanna Sch{\"a}fer and Mehdi Elahi and David Elsweiler and Georg Groh and Morgan Harvey and Bernd Ludwig and Francesco Ricci and Alan Said",

year = "2017",

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booktitle = "Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization - UMAP '17",

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Schäfer, H, Elahi, M, Elsweiler, D, Groh, G, Harvey, M, Ludwig, B, Ricci, F & Said, A 2017, User Nutrition Modelling and Recommendation. in M Tkalcic, D Thakker, P Germanakos, K Yacef, C Paris & O Santos (eds), Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization - UMAP '17. ACM, New York, pp. 93-96, UMAP '17: 25th Conference on User Modeling, Adaptation and Personalization, 9/07/17. https://doi.org/10.1145/3099023.3099108

User Nutrition Modelling and Recommendation. / Schäfer, Hanna; Elahi, Mehdi; Elsweiler, David et al.
Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization - UMAP '17. ed. / Marko Tkalcic; Dhaval Thakker; Panagiotis Germanakos; Kalina Yacef; Cecile Paris; Olgo Santos. New York: ACM, 2017. p. 93-96.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - User Nutrition Modelling and Recommendation

AU - Schäfer, Hanna

AU - Elahi, Mehdi

AU - Elsweiler, David

AU - Groh, Georg

AU - Harvey, Morgan

AU - Ludwig, Bernd

AU - Ricci, Francesco

AU - Said, Alan

PY - 2017/7/9

Y1 - 2017/7/9

N2 - 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.

AB - 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.

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DO - 10.1145/3099023.3099108

M3 - Chapter

SN - 9781450350679

SP - 93

EP - 96

BT - Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization - UMAP '17

A2 - Tkalcic, Marko

A2 - Thakker, Dhaval

A2 - Germanakos, Panagiotis

A2 - Yacef, Kalina

A2 - Paris, Cecile

A2 - Santos, Olgo

PB - ACM

CY - New York

T2 - UMAP '17: 25th Conference on User Modeling, Adaptation and Personalization

Y2 - 9 July 2017

ER -

User Nutrition Modelling and Recommendation

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