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research-article

Health-aware food recommendation system with dual attention in heterogeneous graphs

Authors:

Saman Forouzandeh,

Mehrdad Rostami,

Kamal Berahmand,

Razieh SheikhpourAuthors Info & Claims

Volume 169, Issue C

https://doi.org/10.1016/j.compbiomed.2023.107882

Published: 17 April 2024 Publication History

Abstract

Recommender systems (RS) have been increasingly applied to food and health. However, challenges still remain, including the effective incorporation of heterogeneous information and the discovery of meaningful relationships among entities in the context of food and health recommendations. To address these challenges, we propose a novel framework, the Health-aware Food Recommendation System with Dual Attention in Heterogeneous Graphs (HFRS-DA), for unsupervised representation learning on heterogeneous graph-structured data. HFRS-DA utilizes an attention technique to reconstruct node features and edges and employs a dual hierarchical attention mechanism for enhanced unsupervised learning of attributed graph representations. HFRS-DA addresses the challenge of effectively leveraging the heterogeneous information in the graph and discovering meaningful semantic relationships between entities. The framework analyses recipe components and their neighbours in the heterogeneous graph and can discover popular and healthy recipes, thereby promoting healthy eating habits. We compare HFRS-DA using the Allrecipes dataset and find that it outperforms all the related methods from the literature. Our study demonstrates that HFRS-DA enhances the unsupervised learning of attributed graph representations, which is important in scenarios where labelled data is scarce or unavailable. HFRS-DA can generate node embeddings for unused data effectively, enabling both inductive and transductive learning.

Highlights

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HFRS-DA Recommends users’ popular recipes, that are healthy.

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Utilizes dual attention to identify personalized healthy food recommendations.

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Improves unsupervised learning for better recommendations.

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Generates effective embeddings using meta-path training for accurate recommendations.

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Cited By

Zhang YZhou XZhu FLiu NGuo WXu YShen ZCui LSerra ESpezzano F(2024)Multi-modal Food Recommendation with Health-aware Knowledge DistillationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679580(3279-3289)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679580
Saad HZaki JAbdelsalam M(2024)Employing of machine learning and wearable devices in healthcare system: tasks and challengesNeural Computing and Applications10.1007/s00521-024-10197-z36:29(17829-17849)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s00521-024-10197-z

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cover image Computers in Biology and Medicine

Computers in Biology and Medicine Volume 169, Issue C

Feb 2024

1514 pages

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Copyright © 2023.

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Pergamon Press, Inc.

United States

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Published: 17 April 2024

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Zhang YZhou XZhu FLiu NGuo WXu YShen ZCui LSerra ESpezzano F(2024)Multi-modal Food Recommendation with Health-aware Knowledge DistillationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679580(3279-3289)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679580
Saad HZaki JAbdelsalam M(2024)Employing of machine learning and wearable devices in healthcare system: tasks and challengesNeural Computing and Applications10.1007/s00521-024-10197-z36:29(17829-17849)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s00521-024-10197-z

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