Abstract
In the contemporary world, humans are constantly involved in the race of making their lifestyle better by making all possible efforts to earn more and more money. However, it can be frequently observed that in this hustle for money, he often compromises on his health, and eating on the go has now become a trend that has replaced the infamous cook by your own tradition. Cooking at home by ourselves ensures the best quality of food and that ensures good health, whereas the eat on the go culture has led to more and more consumption of fast food which affects the health of a person thereby indirectly reducing his/her efficiency to work by deteriorating his/her health. Through this paper, we aim to help people around the globe with the problem stated above via a social commerce platform.
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References
Freyne J, Berkovsky S (2010) Intelligent food planning: personalized recipe recommendation. In: Proceedings of the 15th international conference on intelligent user interfaces, Feb 2010, pp 321–324
Kuo FF, Li CT, Shan MK, Lee SY (2012) Intelligent menu planning: recommending set of recipes by ingredients. In: Proceedings of the ACM multimedia 2012 workshop on multimedia for cooking and eating activities, Nov 2012, pp 1–6
Shino N, Yamanishi R, Fukumoto J (2016) Recommendation system for alternative-ingredients based on co-occurrence relation on recipe database and the ingredient category. In: 2016 5th IIAI international congress on advanced applied informatics (IIAI-AAI), July 2016. IEEE, pp 173–178
Ueta T, Iwakami M, Ito T (2011) A recipe recommendation system based on automatic nutrition information extraction. In: International conference on knowledge science, engineering and management, Dec 2011. Springer, Berlin, Heidelberg, pp 79–90
Maheshwari S, Chourey M (2019) Recipe recommendation system using machine learning models. Int Res J Eng Technol (IRJET) 6(9):366–369
Kayes I, Iamnitchi A (2017) Privacy and security in online social networks: a survey. Online Soc Netw Media 3:1–21
Yuan Z, Luo F (2019) Personalized diet recommendation based on K-means and collaborative filtering algorithm. J Phys Conf Ser 1213(3):032013. IOP Publishing
Padillo F, Luna JM, Cano A, Ventura S (2016) A data structure to speed-up machine learning algorithms on massive datasets. In: International conference on hybrid artificial intelligence systems, Apr 2016. Springer, Cham, pp 365–376
De Clercq M, Stock M, De Baets B, Waegeman W (2016) Data-driven recipe completion using machine learning methods. Trends Food Sci Technol 49:1–13
Nilesh N, Kumari M, Hazarika P, Raman V (2019) Recommendation of Indian cuisine recipes based on ingredients. In: 2019 IEEE 35th international conference on data engineering workshops (ICDEW), Apr 2019. IEEE, pp 96–99
Vivek MB, Manju N, Vijay MB (2018) Machine learning based food recipe recommendation system. In: Proceedings of international conference on cognition and recognition. Springer, Singapore, pp 11–19
Chang M, Guillain LV, Jung H, Hare VM, Kim J, Agrawala M (2018) Recipescape: an interactive tool for analyzing cooking instructions at scale. In: Proceedings of the 2018 CHI conference on human factors in computing systems, Apr 2018, pp 1–12
Saravanakumar K, Deepa K (2016) On privacy and security in social media—a comprehensive study. Procedia Comput Sci 78:114–119
Lewis T (2018) Digital food: from paddock to platform. Commun Res Pract 4(3):212–228
Ghahtarani A, Sheikhmohammady M, Rostami M (2020) The impact of social capital and social interaction on customers’ purchase intention, considering knowledge sharing in social commerce context. J Innov Knowl 5(3):191–199
Funk S (2012) Social networks and the promotion of regional food culture
Zhou L, Zhang P, Zimmermann HD (2013) Social commerce research: an integrated view. Electron Commer Res Appl 12(2):61–68
Turban E, Bolloju N, Liang TP (2010) Social commerce: an e-commerce perspective. In: Proceedings of the 12th international conference on electronic commerce: roadmap for the future of electronic business, Aug 2010, pp 33–42
Ueda M, Takahata M, Nakajima S (2011) User’s food preference extraction for personalized cooking recipe recommendation. In: Workshop of ISWC, Oct 2011, pp 98–105
Elsweiler D, Trattner C, Harvey M (2017) Exploiting food choice biases for healthier recipe recommendation. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, Aug 2017, pp 575–584
Starychfojtu J, Peska L (2020) SmartRecepies: towards cooking and food shopping integration via mobile recipes recommender system. In: Proceedings of the 22nd international conference on information integration and web-based applications & services, Nov 2020, pp 144–148
Zhang C, Sun J, Zhu X, Fang Y (2010) Privacy and security for online social networks: challenges and opportunities. IEEE Netw 24(4):13–18
Willis A, Lin E, Zhang B (2017) Forage: optimizing food use with machine learning generated recipes
Svistova SF (2018) Analyzing cooking recipes with machine learning. 8(74)
Forbes P, Zhu M (2011) Content-boosted matrix factorization for recommender systems: experiments with recipe recommendation. In: Proceedings of the fifth ACM conference on recommender systems, Oct 2011, pp 261–264
Sharma PP, Gupta A, Parmar H, Patidar D, Rana APK (2021) Food recipe recommendation system
Garrido-Merchán EC, Albarca-Molina A (2018) Suggesting cooking recipes through simulation and bayesian optimization. In: International conference on intelligent data engineering and automated learning, Nov 2018. Springer, Cham, pp 277–284
Bernardes D. Food recipe recommendation and the social filtering formalism
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Kumar, A., Kotak, B., Pachaury, U., Patel, P., Shah, P. (2023). Social Commerce Platform for Food Enthusiasts with Integrated Recipe Recommendation Using Machine Learning. In: Joshi, A., Mahmud, M., Ragel, R.G. (eds) Information and Communication Technology for Competitive Strategies (ICTCS 2021). Lecture Notes in Networks and Systems, vol 400. Springer, Singapore. https://doi.org/10.1007/978-981-19-0095-2_49
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DOI: https://doi.org/10.1007/978-981-19-0095-2_49
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