Combining data-driven and domain knowledge components in an intelligent assistant to build personalized menus
dc.contributor.author | Sànchez-Marrè, Miquel |
dc.contributor.author | Gibert, Karina |
dc.contributor.author | Sevilla-Villanueva, Beatriz |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa |
dc.date.accessioned | 2020-05-27T08:14:30Z |
dc.date.available | 2020-05-27T08:14:30Z |
dc.date.issued | 2019 |
dc.identifier.citation | Sànchez-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.issn | 0302-9743 |
dc.identifier.uri | http://hdl.handle.net/2117/189154 |
dc.description.abstract | 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. |
dc.description.sponsorship | 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). |
dc.format.extent | 13 p. |
dc.language.iso | eng |
dc.publisher | Springer |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica |
dc.subject.lcsh | Recommender systems (Information filtering) |
dc.subject.lcsh | Menus -- Planning |
dc.subject.lcsh | Ontologies (Information retrieval) |
dc.subject.other | Personalized recommendation |
dc.subject.other | Nutritional plan prescription |
dc.subject.other | Case-based reasoning |
dc.subject.other | Knowledge management |
dc.subject.other | Contextual information |
dc.subject.other | Healthy life-styles |
dc.title | Combining data-driven and domain knowledge components in an intelligent assistant to build personalized menus |
dc.type | Article |
dc.subject.lemac | Menús -- Planificació |
dc.subject.lemac | Sistemes recomanadors (Filtratge d'informació) |
dc.subject.lemac | Ontologies (Informàtica) |
dc.contributor.group | Universitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic |
dc.identifier.doi | 10.1007/978-3-030-19651-6_17 |
dc.description.peerreviewed | Peer Reviewed |
dc.subject.ams | Classificació AMS::68 Computer science::68T Artificial intelligence |
dc.subject.ams | Classificació AMS::90 Operations research, mathematical programming::90C Mathematical programming |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007%2F978-3-030-19651-6_17 |
dc.rights.access | Open Access |
local.identifier.drac | 28508240 |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/AEI/TIN2017-90567-REDT |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO/TIN2014-60557-R Diet4You |
dc.relation.projectid | info:eu-repo/grantAgreement/AGAUR/RIS3CAT/2017 SGR 574 |
local.citation.author | Sànchez-Marrè, M.; Gibert, Karina; Sevilla-Villanueva, Beatriz |
local.citation.publicationName | Lecture notes in computer science |
local.citation.volume | 11487 |
local.citation.startingPage | 167 |
local.citation.endingPage | 179 |
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