{"type":"ScholarlyArticle","title":"RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation","authors":[{"name":"Yijun Tian","slug":"yijun-tian","given":"Yijun","family":"Tian","affiliation":"Department of Computer Science , University of Notre Dame , USA"},{"name":"Chuxu Zhang","slug":"chuxu-zhang","given":"Chuxu","family":"Zhang","affiliation":"Department of Computer Science , Brandeis University , USA"},{"name":"Zhichun Guo","slug":"zhichun-guo","given":"Zhichun","family":"Guo","affiliation":"Department of Computer Science , University of Notre Dame , USA"},{"name":"Chao Huang","slug":"chao-huang","given":"Chao","family":"Huang","affiliation":"Department of Computer Science , University of Hong Kong , Hong Kong"},{"name":"Ronald Metoyer","slug":"ronald-metoyer","given":"Ronald","family":"Metoyer","affiliation":"Department of Computer Science , University of Notre Dame , USA"},{"name":"Nitesh V. Chawla","slug":"nitesh-v-chawla","given":"Nitesh V.","family":"Chawla","affiliation":"Department of Computer Science , University of Notre Dame , USA"}],"abstract":"Recipe recommendation systems play an essential role in helping people decide what to eat. Existing recipe recommendation systems typically focused on content-based or collaborative filtering approaches, ignoring the higher-order collaborative signal such as relational structure information among users, recipes and food items. In this paper, we formalize the problem of recipe recommendation with graphs to incorporate the collaborative signal into recipe recommendation through graph modeling. In particular, we first present URI-Graph, a new and large-scale user-recipe-ingredient graph. We then propose RecipeRec, a novel heterogeneous graph learning model for recipe recommendation. The proposed model can capture recipe content and collaborative signal through a heterogeneous graph neural network with hierarchical attention and an ingredient set transformer. We also introduce a graph contrastive augmentation strategy to extract informative graph knowledge in a self-supervised manner. Finally, we design a joint objective function of recommendation and contrastive learning to optimize the model. Extensive experiments demonstrate that RecipeRec outperforms state-of-the-art methods for recipe recommendation. Dataset and codes are available at https://github.com/meettyj/RecipeRec.","doi":"10.24963/ijcai.2022/481","url":"https://ijcai.org/proceedings/2022/481","pdf":"https://ijcai.org/proceedings/2022/0481.pdf","markdown":"https://ijcai.org/proceedings/2022/481.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/481","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Machine Learning","track":"Main Track","number":481,"pages":{"first":3466,"last":3472},"keywords":["Machine Learning: Applications","Machine Learning: Recommender Systems","Data Mining: Mining Graphs","Data Mining: Mining Heterogenous Data"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986668","length_sec":82,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984811","length_sec":282,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Britto et al., 2020] Larissa Britto, Luciano Pacífico, Emilia Oliveira, and Teresa Ludermir. 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