Abstract
It is stated that the FoodTech web-services market is actively developing, providing a wide variety of formats for fulfilling the client’s needs in individual diet planning. The aim of the work is an overview of the process of business modeling and designing a web service that provides personalized recommendations on an individual diet planning based on the results of the DNA test interpretation. Within the analysis of the adjacent markets, potential competitors were carried out, as well as the target audience of the web service was identified. A general model of the web service and the format for providing services have been obtained, key advantages in comparison with competitive services have been identified. The description of the minimum viable product (MVP) is presented in the form of a business model, a work plan, a list of necessary resources, and required technologies.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Research of the Russian and world FoodTech market: key trends, limitations and prospects (in Russian). https://json.tv/ict_telecom_analytics_view/issledovanie-rossiyskogo-i-mirovogo-rynka-foodtech-klyuchevye-trendy-ogranicheniya-i-perspektivy-20200527010059
Genotek Medical Center - Laboratory of DNA analysis. https://www.genotek.ru/
Atlas Laboratory webpage - Laboratory of DNA analysis. https://www.atlas.ru/
Whelan, K.F.: Advanced Nutrition and Dietetics in Obesity. Wiley, Hoboken (2018)
Osterwalder A., Pigneur Y.: Business model generation. https://tudelft.openresearch.net/image/2015/10/28/business_model_generation.pdf
Federal Center for Hygiene and Epidemiology of Rospotrebnadzor: Norms of physiological needs for energy and nutrients for various groups of the population of the Russian Federation (in Russian)
Shin, D.M.: GEN2VCF: a converter for human genome imputation output format to VCF format. Genes Genomics 42(10), 1163–1168 (2020)
Mokdara, T., Priyakorn, P., Jaturon, H.: Personalized food recommendation using deep neural network. In: 2018 Seventh ICT International Student Project Conference (ICT-ISPC), IEEE (2018)
Freyne, J., Berkovsky, S.: Intelligent food planning: personalized recipe recommendation. In: The 15th Annual Meeting of the Intelligent User Interfaces Community, pp. 321-324 (2010). 7–10 February 2010
Trevisiol, M., Chiarandini, L., Baeza-Yates, R.: Buon appetito - recommending personalized menus. In: 25th ACM Conference on Hypertext and Social Media, pp. 327-239 (2014). 1–4 September 2014
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Rudenko, V.D., Ruban, A.O., Kolesnikov, M.V., Arseniev, A.N., Maximova, T.G. (2021). Personalized Diet Web-Service: Business Modeling and Development. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Software Engineering Application in Informatics. CoMeSySo 2021. Lecture Notes in Networks and Systems, vol 232. Springer, Cham. https://doi.org/10.1007/978-3-030-90318-3_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-90318-3_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-90317-6
Online ISBN: 978-3-030-90318-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)