Improving healthy food recommender systems through heterogeneous hypergraph learning

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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
Published in:	EGYPTIAN INFORMATICS JOURNAL
Main Authors:	Wang, Jing; Zhou, Jincheng; Aksoy, Muammer; Sharma, Nidhi; Rahman, Md Arafatur; Zain, Jasni Mohamad; Alenazi, Mohammed J. F.; Aminzadeh, Aliyeh
Format:	Article
Language:	English
Published:	CAIRO UNIV, FAC COMPUTERS & INFORMATION 2024
Subjects:	Computer Science
Online Access:	https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-recordWOS:001367730700001

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