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

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dc.contributor.authorChavan, Pallavi
dc.contributor.authorThoms, Brian
dc.contributor.authorIsaacs, Jason
dc.date.accessioned2020-12-24T19:46:49Z
dc.date.available2020-12-24T19:46:49Z
dc.date.issued2021-01-05
dc.description.abstractAdvances 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.extent10 pages
dc.identifier.doi10.24251/HICSS.2021.458
dc.identifier.isbn978-0-9981331-4-0
dc.identifier.urihttp://hdl.handle.net/10125/71074
dc.language.isoEnglish
dc.relation.ispartofProceedings of the 54th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectPersonal Health and Wellness Management with Technologies
dc.subjectbig data
dc.subjectnutrition management
dc.subjectpersonal health management
dc.subjectrecommender system
dc.titleA Recommender System for Healthy Food Choices: Building a Hybrid Model for Recipe Recommendations using Big Data Sets
prism.startingpage3774

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