Improving healthy food recommender systems through heterogeneous hypergraph learning

Improving healthy food recommender systems through heterogeneous hypergraph learning

Recommender systems in health-conscious recipe suggestions have evolved rapidly, particularly with the integration of both homogeneous and heterogeneous graphs. However, incorporating IoT devices into healthcare, such as wearable fitness trackers and smart nutrition scales, presents new challenges....

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Bibliographic Details
Main Authors: Jing Wang, Jincheng Zhou, Muammer Aksoy, Nidhi Sharma, Md Arafatur Rahman, Jasni Mohamad Zain, Mohammed J.F. Alenazi, Aliyeh Aminzadeh
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:Egyptian Informatics Journal
Subjects:
Heterogeneous healthy food graph
Healthy food recommender systems
Hypergraph learning
Hypergraph convolution
Hypergraph attention
Online Access:http://www.sciencedirect.com/science/article/pii/S1110866524001336
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