Choice-Based User Interface Design of a Smart Healthy Food Recommender System for Nudging Eating Behavior of Older Adult Patients with Newly Diagnosed Type II Diabetes

Food recommender-systems are considered an effective tool to help users adjust their eating habits and achieve a healthier diet. This paper aims to develop a new hybrid food recommender-system to overcome the shortcomings of previous systems, such as ignoring food ingredients, time factor, cold start users, cold start food items and community aspects. The proposed method involves two phases: food content-based recommendation and user-based recommendation. Graph clustering is used in the first phase, and a deep-learning based approach is used in the second phase to cluster both users and food items. Besides a holistic-like approach is employed to account for time and user-community related issues in a way that improves the quality of the recommendation provided to the user. We compared our model with a set of state-of-the-art recommender-systems using five distinct performance metrics: Precision, Recall, F1, AUC and NDCG. Experiments using dataset extracted from "Allrecipes.com" demonstrated that the developed food recommender-system performed best.

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Food Recognition System and Nutrition Tracking Using Convolutional Neural Network Model

Indhumathi,

et al. 2024

2024 Asia Pacific Conference on Innovation in Technology (APCIT)

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Choice-Based User Interface Design of a Smart Healthy Food Recommender System for Nudging Eating Behavior of Older Adult Patients with Newly Diagnosed Type II Diabetes

Cited by 3 publications

References 34 publications

Food Recommender Systems

Food Recommender Systems

A Novel Time-Aware Food Recommender-System Based on Deep Learning and Graph Clustering

Food Recognition System and Nutrition Tracking Using Convolutional Neural Network Model

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