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Existing methods have limitations: they mainly focus on homogeneous data (e.g., instruction or image), ignoring heterogeneous data (e.g., flavor compound or ingredient), which certainly hurts the classification performance. This is because collecting enough available heterogeneous data of a recipe is a non-trivial task. In this paper, we present a new heterogeneous data augmentation method to improve classification performance. Specifically, we first construct a heterogeneous recipe graph network to represent heterogeneous data, which includes four main-stream types of heterogeneous data: ingredient, flavor compound, image, and instruction. Then, we draw a sequence of augmented graphs for Semi-Supervised learning through multinomial sampling. The probability distribution of sampling depends on the\n            <jats:italic>Cosine<\/jats:italic>\n            distance between the nodes of graph. In this way, we name our approach as\n            <jats:italic>Multinomial Sampling Graph Data Augmentation<\/jats:italic>\n            (MS-GDA). Extensive experiments demonstrate that MS-GDA significantly outperforms SOTA baselines on cuisine classification and region prediction with the recipe benchmark dataset. Code is available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/LiangzheChen\/MS-GDA\">https:\/\/github.com\/LiangzheChen\/MS-GDA<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3648620","type":"journal-article","created":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T12:29:51Z","timestamp":1708432191000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["MS-GDA: Improving Heterogeneous Recipe Representation via Multinomial Sampling Graph Data Augmentation"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6580-0592","authenticated-orcid":false,"given":"Liangzhe","family":"Chen","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence and Computer Science &amp; Jiangsu Key Laboratory of Media Design and Software Technology &amp; Engineering Research Center of Intelligent Technology for Healthcare, Ministry of Education, Jiangnan University, Wuxi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3135-0447","authenticated-orcid":false,"given":"Wei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer Science &amp; Jiangsu Key Laboratory of Media Design and Software Technology &amp; Engineering Research Center of Intelligent Technology for Healthcare, Ministry of Education, Jiangnan University, Wuxi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6079-009X","authenticated-orcid":false,"given":"Xiaohui","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Wuhan University, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0626-6240","authenticated-orcid":false,"given":"Zhenyu","family":"Wang","sequence":"additional","affiliation":[{"name":"JiaXing Institute of Future Food, Jiaxing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1219-4386","authenticated-orcid":false,"given":"Stefano","family":"Berretti","sequence":"additional","affiliation":[{"name":"Information Engineering, University of Firenze, Firenze, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7013-9081","authenticated-orcid":false,"given":"Shaohua","family":"Wan","sequence":"additional","affiliation":[{"name":"Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China"}]}],"member":"320","published-online":{"date-parts":[[2024,4,25]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"3197","DOI":"10.1145\/2702123.2702153","volume-title":"Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems","author":"Abbar Sofiane","year":"2015","unstructured":"Sofiane Abbar, Yelena Mejova, and Ingmar Weber. 2015. 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