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Combining Data-Driven and Domain Knowledge Components in an Intelligent Assistant to Build Personalized Menus

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 11487)


In this paper, some new components that have been integrated in the Diet4You system for the generation of nutritional plans are introduced. Negative user preferences have been modelled and introduced in the system. Furthermore, the cultural eating styles originated from the location where the user lives have been taken into account dividing the original menu plan in sub-plans. Each sub-plan is in charge to optimize one of the meals of one day in the personal menu of the user. The main latent reasoning mechanism used is case-based reasoning, which reuses previous menu configurations according to the nutritional plan and the corresponding hard constraints and the user preferences to meet a personalized recommendation menu for a given user. It uses the cognitive analogical reasoning technique in addition to ontologies, nutritional databases and expert knowledge. The preliminary results with some examples of application to test the new contextual components have been very satisfactory according to the evaluation of the experts.


  • Personalized recommendation
  • Nutritional plan prescription
  • Case-Based Reasoning
  • Knowledge management
  • Contextual information
  • Healthy life-styles

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  • DOI: 10.1007/978-3-030-19651-6_17
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This work has been partially supported by the project Diet4You (TIN2014-60557-R), the Spanish Thematic Network MAPAS [TIN2017-90567-REDT (MINECO/FEDER EU)], and the Consolidated Research Group Grant from AGAUR (Generalitat de Catalunya) IDEAI-UPC (AGAUR SGR2017-574).

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Correspondence to Miquel Sànchez-Marrè .

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Sànchez-Marrè, M., Gibert, K., Sevilla-Villaneva, B. (2019). Combining Data-Driven and Domain Knowledge Components in an Intelligent Assistant to Build Personalized Menus. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) From Bioinspired Systems and Biomedical Applications to Machine Learning. IWINAC 2019. Lecture Notes in Computer Science(), vol 11487. Springer, Cham.

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