Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/71074

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

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Title:A Recommender System for Healthy Food Choices: Building a Hybrid Model for Recipe Recommendations using Big Data Sets
Authors:Chavan, Pallavi
Thoms, Brian
Isaacs, Jason
Keywords:Personal Health and Wellness Management with Technologies
big data
nutrition management
personal health management
recommender system
Date Issued:05 Jan 2021
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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/71074
ISBN:978-0-9981331-4-0
DOI:10.24251/HICSS.2021.458
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Personal Health and Wellness Management with Technologies


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