Food recommendation with graph convolutional network
| Autor: | Fuli Feng, Tian Lan, Xiaoyan Gao, Xian-Ling Mao, Zewen Chi, Heyan Huang |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Information Systems and Management
Exploit Computer science business.industry Ranging Machine learning computer.software_genre Bridge (interpersonal) Computer Science Applications Theoretical Computer Science Artificial Intelligence Control and Systems Engineering Key (cryptography) Collaborative filtering Graph (abstract data type) Embedding Artificial intelligence business Feature learning computer Software |
| Zdroj: | Information Sciences. 584:170-183 |
| ISSN: | 0020-0255 |
| DOI: | 10.1016/j.ins.2021.10.040 |
| Popis: | Food recommendation has attracted increasing attentions to various food-related applications and services. The food recommender models aim to match users’ preferences with recipes, where the key lies in the representation learning of users and recipes. However, ranging from early content-based filtering and collaborative filtering methods to recent hybrid methods , the existing work overlooks the various food-related relations, especially the ingredient-ingredient relations, leading to incomprehensive representations. To bridge this gap, we propose a novel model Food recommendation with Graph Convolutional Network (FGCN), which exploits ingredient-ingredient, ingredient-recipe, and recipe-user relations deeply. FGCN employs the information propagation mechanism and adopts multiple embedding propagation layers to model high-order connectivity across different food-related relations and enhance the representations. Specifically, we develop three types of information propagation: (1) ingredient-ingredient information propagation, (2) ingredient-recipe information propagation, and (3) recipe-user information propagation. To validate the effectiveness and rationality of FGCN, we conduct extensive experiments on a real-world dataset. The results show that the proposed FGCN outperforms the state-of-the-art baselines. Further in-depth analyses reveal that FGCN could alleviate the sparsity issue in food recommendation. |
| Databáze: | OpenAIRE |
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