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Self-supervised Calorie-aware Heterogeneous Graph Networks for Food Recommendation | |
Song, Yaguang1,2![]() ![]() ![]() | |
发表期刊 | ACM Transactions on Multimedia Computing, Communications, and Applications
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2023-02-03 | |
卷号 | 19期号:1s页码:1-23 |
文章类型 | 期刊论文 |
摘要 | With the rapid development of online recipe sharing platforms, food recommendation is emerging as an important application. Although recent studies have made great progress on food recommendation, they have two shortcomings that are likely to affect the recommendation performance. (1) The relations between ingredients are not considered, which may lead to sub-optimal representations of recipes and further result in the neglect of the user’s personalized ingredient combination preference. (2) Existing methods do not consider the impact of users’ preferences on calories in users’ food decision-making process. In this article, we propose a Self-supervised Calorie-aware Heterogeneous Graph Network (SCHGN) to model the relations between ingredients and incorporate calories of food simultaneously. Specifically, we first incorporate users, recipes, ingredients, and calories into a heterogeneous graph and explicitly present the complex relations among them with directed edges. Then, we explore the co-occurrence relation of ingredients in different recipes via self-supervised ingredient prediction. To capture users’ dynamic preferences on calories of food, we learn calorie-aware user representations by hierarchical message passing and compute a comprehensive user-guided recipe representation by attention mechanism. The final food recommendation is accomplished based on the similarity between a user’s calorie-aware representation and the user-guided representation of a recipe. Extensive experiment results on benchmark datasets demonstrate the effectiveness of the proposed method. |
关键词 | Food recommendation recipe calories heterogeneous graph selfsupervised learning |
URL | 查看原文 |
收录类别 | SCI |
语种 | 英语 |
七大方向——子方向分类 | 多模态智能 |
国重实验室规划方向分类 | 多模态协同认知 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/51955 |
专题 | 多模态人工智能系统全国重点实验室_多媒体计算 多模态人工智能系统全国重点实验室 |
通讯作者 | Xu, Changsheng |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CASIA) 2.School of Artificial Intelligence, University of Chinese Academy of Sciences(UCAS) 3.Peng Cheng Laboratory |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Song, Yaguang,Yang, Xiaoshan,Xu, Changsheng. Self-supervised Calorie-aware Heterogeneous Graph Networks for Food Recommendation[J]. ACM Transactions on Multimedia Computing, Communications, and Applications,2023,19(1s):1-23. |
APA | Song, Yaguang,Yang, Xiaoshan,&Xu, Changsheng.(2023).Self-supervised Calorie-aware Heterogeneous Graph Networks for Food Recommendation.ACM Transactions on Multimedia Computing, Communications, and Applications,19(1s),1-23. |
MLA | Song, Yaguang,et al."Self-supervised Calorie-aware Heterogeneous Graph Networks for Food Recommendation".ACM Transactions on Multimedia Computing, Communications, and Applications 19.1s(2023):1-23. |
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Self-supervised Calo(1381KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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