Diabetic-friendly multi-agent recommendation system for restaurants based on social media sentiment analysis and multi-criteria decision making

dc.contributor.authorTeixeira, Brunopor
dc.contributor.authorMartinho, Diogopor
dc.contributor.authorNovais, Paulopor
dc.contributor.authorCorchado, Juanpor
dc.contributor.authorMarreiros, Goretipor
dc.date.accessioned2023-09-14T08:18:17Z
dc.date.embargo10000-01-01
dc.date.issued2022
dc.date.updated2023-07-31T23:42:43Z
dc.description.abstractLifestyle, poor diet, stress, among other factors, strongly contribute to aggravate people's health problems, such as diabetes and high blood pressure. Some of these problems could be avoided if some of the essential recommendations for the practice of a healthy lifestyle were followed. The paper proposes a solution designed for diabetic people to find restaurants nearby that are more suitable for their health needs. A diabetic-friendly feature that will use a set of criteria, built through a Multi-Agent System (MAS) that using the user preferences initially recorded, will provide the user with three category recommendations that potentially benefit the user lifestyle and health. The solution proposes the use of Case-Based Reasoning algorithm to enable the solution to evolve and improve in each interaction with the user. Sentiment Analysis was also used for identifying the restaurant reviews score, since this is one of the defined criteria for the solution.por
dc.description.sponsorshipThis research work was developed under the project Food Friend -"Autonomous and easy-to-use tool for monitoring of personal food intake and personalized feedback" (ITEA 18032), co-financed by the North Regional Operational Program (NORTE 2020) under the Portugal 2020 and the European Regional Development Fund (ERDF), with the reference NORTE-01-0247-FEDER-047381 and by National Funds through FCT (Fundacao para a Ciencia e a Tecnologia) under the project UIDB/00760/2020.por
dc.distributioninternationalpor
dc.identifier.citationTeixeira, B., Martinho, D., Novais, P., Corchado, J., Marreiros, G. (2022). Diabetic-Friendly Multi-agent Recommendation System for Restaurants Based on Social Media Sentiment Analysis and Multi-criteria Decision Making. In: Marreiros, G., Martins, B., Paiva, A., Ribeiro, B., Sardinha, A. (eds) Progress in Artificial Intelligence. EPIA 2022. Lecture Notes in Computer Science(), vol 13566. Springer, Cham. https://doi.org/10.1007/978-3-031-16474-3_30por
dc.identifier.doi10.1007/978-3-031-16474-3_30por
dc.identifier.eisbn978-3-031-16474-3
dc.identifier.isbn978-3-031-16473-6
dc.identifier.issn0302-9743
dc.identifier.urihttps://hdl.handle.net/1822/86378
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherSpringer, Champor
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00760%2F2020/PTpor
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007/978-3-031-16474-3_30por
dc.rightsrestrictedAccesspor
dc.subjectRecommender systempor
dc.subjectDiabetic-friendlypor
dc.subjectMulti-agent systempor
dc.subjectSentiment analysispor
dc.subjectMulti-criteria decision makingpor
dc.subjectCase-based reasoningpor
dc.subjectk-Nearest Neighborspor
dc.subject.fosCiências Naturais::Ciências da Computação e da Informaçãopor
dc.subject.wosScience & Technology
dc.titleDiabetic-friendly multi-agent recommendation system for restaurants based on social media sentiment analysis and multi-criteria decision makingpor
dc.typeconferencePaperpor
dspace.entity.typePublicationen
oaire.citationEndPage373por
oaire.citationStartPage361por
oaire.citationVolume13566por
sdum.bookTitlePROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2022por
sdum.conferencePublicationPROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2022por
sdum.export.identifier12667
sdum.journalLecture Notes in Artificial Intelligence (subseries of Lecture Notes in Computer Science)por

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