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dc.contributor.authorSànchez-Marrè, Miquel
dc.contributor.authorGibert, Karina
dc.contributor.authorSevilla-Villanueva, Beatriz
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa
dc.date.accessioned2020-05-27T08:14:30Z
dc.date.available2020-05-27T08:14:30Z
dc.date.issued2019
dc.identifier.citationSànchez-Marrè, M.; Gibert, K.; Sevilla-Villanueva, B. Combining data-driven and domain knowledge components in an intelligent assistant to build personalized menus. "Lecture notes in computer science", 2019, vol. 11487, p. 167-179.
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/2117/189154
dc.description.abstractIn 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.
dc.description.sponsorshipThis 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).
dc.format.extent13 p.
dc.language.isoeng
dc.publisherSpringer
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subjectÀrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica
dc.subject.lcshRecommender systems (Information filtering)
dc.subject.lcshMenus -- Planning
dc.subject.lcshOntologies (Information retrieval)
dc.subject.otherPersonalized recommendation
dc.subject.otherNutritional plan prescription
dc.subject.otherCase-based reasoning
dc.subject.otherKnowledge management
dc.subject.otherContextual information
dc.subject.otherHealthy life-styles
dc.titleCombining data-driven and domain knowledge components in an intelligent assistant to build personalized menus
dc.typeArticle
dc.subject.lemacMenús -- Planificació
dc.subject.lemacSistemes recomanadors (Filtratge d'informació)
dc.subject.lemacOntologies (Informàtica)
dc.contributor.groupUniversitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic
dc.identifier.doi10.1007/978-3-030-19651-6_17
dc.description.peerreviewedPeer Reviewed
dc.subject.amsClassificació AMS::68 Computer science::68T Artificial intelligence
dc.subject.amsClassificació AMS::90 Operations research, mathematical programming::90C Mathematical programming
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007%2F978-3-030-19651-6_17
dc.rights.accessOpen Access
local.identifier.drac28508240
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/AEI/TIN2017-90567-REDT
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/TIN2014-60557-R Diet4You
dc.relation.projectidinfo:eu-repo/grantAgreement/AGAUR/RIS3CAT/2017 SGR 574
local.citation.authorSànchez-Marrè, M.; Gibert, Karina; Sevilla-Villanueva, Beatriz
local.citation.publicationNameLecture notes in computer science
local.citation.volume11487
local.citation.startingPage167
local.citation.endingPage179


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