{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T07:56:31Z","timestamp":1778313391595,"version":"3.51.4"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:p>Learning effective recipe representations is essential in food studies. Unlike what has been developed for image-based recipe retrieval or learning structural text embeddings, the combined effect of multi-modal information (i.e., recipe images, text, and relation data) receives less attention. In this paper, we formalize the problem of multi-modal recipe representation learning to integrate the visual, textual, and relational information into recipe embeddings. In particular, we first present Large-RG, a new recipe graph data with over half a million nodes, making it the largest recipe graph to date. We then propose Recipe2Vec, a novel graph neural network based recipe embedding model to capture multi-modal information. Additionally, we introduce an adversarial attack strategy to ensure stable learning and improve performance. Finally, we design a joint objective function of node classification and adversarial learning to optimize the model. Extensive experiments demonstrate that Recipe2Vec outperforms state-of-the-art baselines on two classic food study tasks, i.e., cuisine category classification and region prediction. Dataset and codes are available at https:\/\/github.com\/meettyj\/Recipe2Vec.<\/jats:p>","DOI":"10.24963\/ijcai.2022\/482","type":"proceedings-article","created":{"date-parts":[[2022,7,16]],"date-time":"2022-07-16T02:55:56Z","timestamp":1657940156000},"page":"3473-3479","source":"Crossref","is-referenced-by-count":12,"title":["Recipe2Vec: Multi-modal Recipe Representation Learning with Graph Neural Networks"],"prefix":"10.24963","author":[{"given":"Yijun","family":"Tian","sequence":"first","affiliation":[{"name":"University of Notre Dame"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuxu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Brandeis University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhichun","family":"Guo","sequence":"additional","affiliation":[{"name":"University of Notre Dame"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yihong","family":"Ma","sequence":"additional","affiliation":[{"name":"University of Notre Dame"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ronald","family":"Metoyer","sequence":"additional","affiliation":[{"name":"University of Notre Dame"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nitesh V.","family":"Chawla","sequence":"additional","affiliation":[{"name":"University of Notre Dame"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}","theme":"Artificial Intelligence","location":"Vienna, Austria","acronym":"IJCAI-2022","number":"31","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2022,7,23]]},"end":{"date-parts":[[2022,7,29]]}},"container-title":["Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T11:09:57Z","timestamp":1658142597000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2022\/482"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2022\/482","relation":{},"subject":[],"published":{"date-parts":[[2022,7]]}}}