Towards a knowledge-aware food recommender system exploiting holistic user models

Cataldo Musto, Christoph Trattner, Alain D. Starke, Giovanni Semeraro

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

1 Citaat (Scopus)

Samenvatting

Food recommender systems typically rely on popularity, as well as similarity between recipes to generate personalized suggestions. However, this leaves little room for users to explore new preferences, such as to adopt healthier eating habits. In this short paper, we present a recommendation strategy based on knowledge about food and users' health-related characteristics to generate personalized recipes suggestions. By focusing on personal factors as a user's BMI and dietary constraints, we exploited a holistic user model to re-rank a basic recommendation list of 4,671 recipes, and investigated in a web-based experiment (N=200) to what extent it generated satisfactory food recommendations. We found that some of the information encoded in a users' holistic user profiles affected their preferences, thus providing us with interesting findings to continue this line of research.

Originele taal-2Engels
TitelUMAP 2020 - Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization
UitgeverijAssociation for Computing Machinery, Inc
Pagina's333-337
Aantal pagina's5
ISBN van elektronische versie9781450368612
DOI's
StatusGepubliceerd - 7 jul 2020
Evenement28th ACM Conference on User Modeling, Adaptation and Personalization: virtual conference - Genoa, Italië
Duur: 12 jul 202018 jul 2020

Congres

Congres28th ACM Conference on User Modeling, Adaptation and Personalization
Verkorte titelUMAP'20
LandItalië
StadGenoa
Periode12/07/2018/07/20

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  • Citeer dit

    Musto, C., Trattner, C., Starke, A. D., & Semeraro, G. (2020). Towards a knowledge-aware food recommender system exploiting holistic user models. In UMAP 2020 - Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization (blz. 333-337). Association for Computing Machinery, Inc. https://doi.org/10.1145/3340631.3394880