A Recommender System for Healthy Food Choices: Building a Hybrid Model for Recipe Recommendations using Big Data Sets

dc.contributor.author Chavan, Pallavi
dc.contributor.author Thoms, Brian
dc.contributor.author Isaacs, Jason
dc.date.accessioned 2020-12-24T19:46:49Z
dc.date.available 2020-12-24T19:46:49Z
dc.date.issued 2021-01-05
dc.description.abstract Advances in Big Data analytics and machine learning have offered intangible benefits across many areas of one’s health. One such area is a move towards healthier lifestyle choices such as one’s diet. Recommender systems apply techniques that can filter information and narrow that information down based on user preferences or user needs and help users choose what information is relevant. Commonly adopted across e-commerce sites, social networking and entertainment industries, recommender systems can also support nutrition-based health management, offering individuals more food options, not only based on one’s preferred tastes but also on one’s dietary needs and restrictions. This research presents the design, implementation and evaluation of three recommender systems using content-based, collaborative filtering and hybrid recommendation models within the nutrition domain.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2021.458
dc.identifier.isbn 978-0-9981331-4-0
dc.identifier.uri http://hdl.handle.net/10125/71074
dc.language.iso English
dc.relation.ispartof Proceedings of the 54th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Personal Health and Wellness Management with Technologies
dc.subject big data
dc.subject nutrition management
dc.subject personal health management
dc.subject recommender system
dc.title A Recommender System for Healthy Food Choices: Building a Hybrid Model for Recipe Recommendations using Big Data Sets
prism.startingpage 3774
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