Personalized recipe recommendation based on heterogeneous graph neural networks

Yanqian Xie; Yushan Zhang

doi:10.1117/12.3034936

11 July 2024 Personalized recipe recommendation based on heterogeneous graph neural networks

Yanqian Xie, Yushan Zhang

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 1321006 (2024) https://doi.org/10.1117/12.3034936
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

Recipe recommendations are highly sought after in today's society, however, accurate personalized recommendations remain a challenge. Our research focuses on improving traditional recipe recommendation by introducing heterogeneous graph neural networks to mine the information of higher-order co-signals, such as ingredients and tastes, in order to improve recommendation accuracy. First, by constructing a recipe information graph, we achieve effective integration of multi-layered information such as users, recipes and ingredients. In addition, we combine the flavor information to find out the cuisine preferences of different groups of people and identify potential audience groups. Finally, the model effectiveness is improved by self-supervised learning of graphs. Experimental results on a large-scale food recommendation dataset show that the method improves over the optimal baseline method in four metrics, namely, Precision@10, HitRate@10, NDCG@10, and MAP@10, thus demonstrating the effectiveness of the method.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yanqian Xie and Yushan Zhang "Personalized recipe recommendation based on heterogeneous graph neural networks", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 1321006 (11 July 2024); https://doi.org/10.1117/12.3034936

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