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Diabetic-Friendly Multi-agent Recommendation System for Restaurants Based on Social Media Sentiment Analysis and Multi-criteria Decision Making

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Abstract

Lifestyle, 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.

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References

  1. World Health Organization: Global report on diabetes. World Health Organization (2016)

    Google Scholar 

  2. Bertozzi, D., Dimitrakopoulos, G., Flich, J., Sonntag, S.: The fast evolving landscape of on-chip communication (2014)

    Google Scholar 

  3. Ricci, F., Rokach, L., Shapira, B., Kantor, P.B.: Recommender Systems Handbook. Springer, Heidelberg (2010)

    MATH  Google Scholar 

  4. Kahraman, C., Onar, S.C., Oztaysi, B.: Fuzzy multicriteria decision-making: a literature review. Int. J. Comput. Intell. Syst.8, 637–666 (2015)

    Article  Google Scholar 

  5. Andronico, A., Carbonaro, A., Casadei, G.: Integrating a multi-agent recommendation system into a mobile learning management system (2003)

    Google Scholar 

  6. Morais, A.J., Oliveira, E., Jorge, A.M.: A multi-agent recommender system. In: Omatu, S., De Paz Santana, J.F., González, S.R., Molina, J.M., Bernardos, A.M., Rodríguez, J.M. (eds.) Distributed Computing and Artificial Intelligence. AISC, vol. 151, pp. 281–288. Springer, Heidelberg (2012).https://doi.org/10.1007/978-3-642-28765-7_33

    Chapter  Google Scholar 

  7. Marivate, V.N., Ssali, G., Marwala, T.: An intelligent multi-agent recommender system for human capacity building (2008)

    Google Scholar 

  8. Wooldridge, M., Jennings, N.R.: Agent theories, architectures, and languages: a survey. In: Wooldridge, M.J., Jennings, N.R. (eds.) ATAL 1994. LNCS, vol. 890, pp. 1–39. Springer, Heidelberg (1995).https://doi.org/10.1007/3-540-58855-8_1

    Chapter  Google Scholar 

  9. Deng, Z., Zhu, X., Cheng, D., Zong, M., Zhang, S.: Efficient kNN classification algorithm for bid data. Neurocomputing195, 143–148 (2016)

    Article  Google Scholar 

  10. GOAI Applications.https://gaia.fdi.ucm.es/research/colibri/jcolibri/. Accessed 20 June 2022

  11. Faia, R., Pinto, T., Vale, Z.: Dynamic fuzzy clustering method for decision support in electricity markets negotiation, vol. 5 (2016)

    Google Scholar 

  12. Zhu, C., Wang, Z., Gao, D.: New design goal of a classifier: global and local structural risk minimization. Knowl.-Based Syst.100, 25–49 (2016)

    Article  Google Scholar 

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Acknowledgments

This 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 (Fundação para a Ciência e a Tecnologia) under the project UI/DB/00760/2020.

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Authors and Affiliations

  1. Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), Institute of Engineering, Polytechnic of Porto, Porto, Portugal

    Bruno Teixeira, Diogo Martinho & Goreti Marreiros

  2. ALGORITMI Centre, University of Minho, Guimarães, Portugal

    Paulo Novais

  3. BISITE Digital Innovation Hub, University of Salamanca, Edificio Multiusos, Salamanca, Spain

    Juan Corchado

Authors
  1. Bruno Teixeira

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  2. Diogo Martinho

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  3. Paulo Novais

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  4. Juan Corchado

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  5. Goreti Marreiros

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Corresponding author

Correspondence toBruno Teixeira.

Editor information

Editors and Affiliations

  1. ISEP/GECAD, Polytechnic Institute of Porto, Porto, Portugal

    Goreti Marreiros

  2. IST/INESC-ID, University of Lisbon, Lisbon, Portugal

    Bruno Martins

  3. IST/INESC-ID, University of Lisbon, Porto Salvo, Portugal

    Ana Paiva

  4. CISUC, University of Coimbra, Coimbra, Portugal

    Bernardete Ribeiro

  5. IST/INESC-ID, University of Lisbon, Porto Salvo, Portugal

    Alberto Sardinha

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Teixeira, 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_30

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eBook
JPY 5719
Price includes VAT (Japan)
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