KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Diet-Right: A Smart Food Recommendation System

  • Rehman, Faisal (Department of Computer Sciences, COMSATS Institute of Information Technology) ;
  • Khalid, Osman (Department of Computer Sciences, COMSATS Institute of Information Technology) ;
  • Haq, Nuhman ul (Department of Computer Sciences, COMSATS Institute of Information Technology) ;
  • Khan, Atta ur Rehman (College of Computer & Information Sciences, King Saud University) ;
  • Bilal, Kashif (Department of Computer Sciences, COMSATS Institute of Information Technology) ;
  • Madani, Sajjad A. (Department of Computer Sciences, COMSATS Institute of Information Technology)
  • Received : 2016.10.21
  • Accepted : 2017.02.14
  • Published : 2017.06.30
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Abstract

Inadequate and inappropriate intake of food is known to cause various health issues and diseases. Due to lack of concise information about healthy diet, people have to rely on medicines instead of taking preventive measures in food intake. Due to diversity in food components and large number of dietary sources, it is challenging to perform real-time selection of diet patterns that must fulfill one's nutrition needs. Particularly, selection of proper diet is critical for patients suffering from various diseases. In this article, we highlight the issue of selection of proper diet that must fulfill patients' nutrition requirements. To address this issue, we present a cloud based food recommendation system, called Diet-Right, for dietary recommendations based on users' pathological reports. The model uses ant colony algorithm to generate optimal food list and recommends suitable foods according to the values of pathological reports. Diet-Right can play a vital role in controlling various diseases. The experimental results show that compared to single node execution, the convergence time of parallel execution on cloud is approximately 12 times lower. Moreover, adequate accuracy is attainable by increasing the number of ants.

Keywords

References

  1. Oh, Yoosoo, Ahyoung Choi, and Woontack Woo, "u-BabSang: a context-aware food recommendation system," The Journal of Supercomputing 54, no. 1, 61-81, 2010. https://doi.org/10.1007/s11227-009-0314-5
  2. A global agenda for combating malnutrition: progress report. Geneva, World Health Organization, 2000.
  3. Childhood nutrition and progress in implementing the International Code of Marketing of Breast-milk Substitutes. Geneva, World Health Organization, 2002.
  4. WHO, Joint, and FAO Expert Consultation, "Diet, nutrition and the prevention of chronic diseases," World Health Organ Tech Rep Ser 916, no. i-viii, 2003.
  5. WHO/UNICEF. Global prevalence of vitamin A deficiency, MDIS Working Paper No. 2. Geneva, World Health Organization, 1995.
  6. WHO/UNICEF/United Nations University. Iron deficiency anaemia assessment, prevention and control: a guide for programme managers, Geneva, World Health Organization, 2001.
  7. WHO/UNICEF/International Council for the Control of Iodine Deficiency Disorders, Progress towards the elimination of iodine deficiency disorders (IDD). Geneva, World Health Organization, 1999.
  8. Phanich, M., Pholkul, P., & Phimoltares, S., "Food recommendation system using clustering analysis for diabetic patients," in Proc. of International Conference on Information Science and Applications, pp. 1-8, IEEE, April 2010.
  9. Ge, M., Elahi, M., Fernaandez-Tobias, I., Ricci, F., & Massimo, D., "Using tags and latent factors in a food recommender system," in Proc. of the 5th International Conference on Digital Health, pp. 105-112, ACM., May 2015.
  10. Freyne, J., & Berkovsky, S., "Evaluating recommender systems for supportive technologies," User Modeling and Adaptation for Daily Routines, pp. 195-217, Springer London, 2013.
  11. Runo, M., "FooDroid: A Food Recommendation App for University Canteens," Unpublished semester thesis, Swiss Federal Institute of Theology, Zurich, 2011.
  12. Su, C. J., Chen, Y. A., & Chih, C. W., "Personalized ubiquitous diet plan service based on ontology and web services," International Journal of Information and Education Technology, 3(5), 522, 2013.
  13. Evert, A. B., Boucher, J. L., Cypress, M., Dunbar, S. A., Franz, M. J., Mayer-Davis, E. J., & Yancy, W. S., "Nutrition therapy recommendations for the management of adults with diabetes," Diabetes care, 37(Supplement 1), S120-S143, 2014. https://doi.org/10.2337/dc14-S120
  14. LeFevre, M. L., "Behavioral counseling to promote a healthful diet and physical activity for cardiovascular disease prevention in adults with cardiovascular risk factors: US Preventive Services Task Force Recommendation Statement, Annals of internal medicine, 161(8), 587-593, 2014. https://doi.org/10.7326/M14-1796
  15. Teng, C. Y., Lin, Y. R., & Adamic, L. A., "Recipe recommendation using ingredient networks," in Proc. of the 4th Annual ACM Web Science Conference, pp. 298-307, ACM, June 2012.
  16. Rittinghouse, J. W., & Ransome, J. F., "Cloud computing: implementation, management, and security," CRC press, 2016.
  17. Shaukat Khanum Laboratory, https://shaukatkhanum.org.pk/, accessed December 13, 2016.
  18. Agha Khan Laboratory, https://www.aku.edu/labreports/Pages/default.aspx, accessed December 13, 2016
  19. Healthways Laboratory, http://www.healthwayslabs.net, accessed December 13, 2016.
  20. COFIDS Dataset, https://www.gov.uk/government/publications/composition-of-foods-integrated-dataset-cofid, accessed September 03, 2016.
  21. Forbes, P., & Zhu, M., "Content-boosted matrix factorization for recommender systems: experiments with recipe recommendation," in Proc. of the fifth ACM conference on Recommender systems, pp. 261-264, ACM, October 2011.
  22. Freyne, J., & Berkovsky, S., "Intelligent food planning: personalized recipe recommendation," in Proc. of the 15th international conference on Intelligent user interfaces, pp. 321-324, ACM, February 2010.
  23. Geleijnse, G., Nachtigall, P., van Kaam, P., & Wijgergangs, L., "A personalized recipe advice system to promote healthful choices," in Proc. of the 16th international conference on Intelligent user interfaces, pp. 437-438, ACM, February 2011.
  24. Svensson, M., Höök, K., & Cöster, R., "Designing and evaluating kalas: A social navigation system for food recipes," ACM Transactions on Computer-Human Interaction (TOCHI), 12(3), 374-400, 2005. https://doi.org/10.1145/1096737.1096739
  25. Nutrino, URL: https://nutrino.co/, accessed October 10, 2016.
  26. Ueda, M., Asanuma, S., Miyawaki, Y., & Nakajima, S. "Recipe recommendation method by considering the user's preference and ingredient quantity of target recipe," in Proc. of the International MultiConference of Engineers and Computer Scientists, Vol. 1, 2014.
  27. Van Pinxteren, Y., Geleijnse, G., & Kamsteeg, P., "Deriving a recipe similarity measure for recommending healthful meals," in Proc. of the 16th international conference on Intelligent user interfaces, pp. 105-114, ACM, February 2011.
  28. Yang, L., Hsieh, C. K., Yang, H., Dell, N., Belongie, S., & Estrin, D., "Yum-me: Personalized Healthy Meal Recommender System," arXiv preprint arXiv, 1605.07722, 2016.
  29. ShopWell, http://www.shopwell.com/, accessed October 10. 2016.
  30. Yummly, http://developer.yummly.com, accessed October 10. 2016.
  31. Jung, H., & Chung, K., "Knowledge-based dietary nutrition recommendation for obese management," Information Technology and Management, 17(1), 29-42, 2016. https://doi.org/10.1007/s10799-015-0218-4
  32. De Pessemier, T., Dooms, S., & Martens, L., "A food recommender for patients in a care facility, in Proc. of the 7th ACM conference on Recommender systems, pp. 209-212, ACM, October 2013.
  33. DeleVacq, A., Delisle, P., Gravel, M., & Krajecki, M., "Parallel ant colony optimization on graphics processing units," Journal of Parallel and Distributed Computing, 73(1), 52-61. 2013. https://doi.org/10.1016/j.jpdc.2012.01.003
  34. Matlab Parallel Cloud, http://www.mathworks.com/products/parallel-computing/matlab-parallel-cloud/, accessed October 10. 2016.

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