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Traditional personalized tag recommendation systems are limited by the problem of data sparsity, making the personalized tag recommendation models unable to accurately learn the embeddings of users, items, and tags. To address this issue, we propose a contrastive learning-based personalized tag recommendation algorithm, namely CLPTR. Specifically, CLPTR generates augmented views of user\u2013tag and item\u2013tag interaction graphs by injecting noises into implicit feature representations rather than dropping nodes and edges. Hence, CLPTR is able to greatly preserve the underlying semantics of the original user\u2013tag or the item\u2013tag interaction graphs and avoid destroying their structural information. In addition, we integrate the contrastive learning module into a graph neural network-based personalized tag recommendation model, which enables the model to extract self-supervised signals from user\u2013tag and item\u2013tag interaction graphs. We conduct extensive experiments on real-world datasets, and the experimental results demonstrate the state-of-the-art performance of our proposed CLPTR compared with traditional personalized tag recommendation models.<\/jats:p>","DOI":"10.3390\/s24186061","type":"journal-article","created":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T08:12:27Z","timestamp":1726733547000},"page":"6061","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Contrastive Learning-Based Personalized Tag Recommendation"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-4439-1320","authenticated-orcid":false,"given":"Aoran","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2587-8090","authenticated-orcid":false,"given":"Yonghong","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Tongda, Nanjing University of Posts and Telecommunication, Yangzhou 225127, China"}]},{"given":"Shenglong","family":"Li","sequence":"additional","affiliation":[{"name":"College of Tongda, Nanjing University of Posts and Telecommunication, Yangzhou 225127, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7935-7173","authenticated-orcid":false,"given":"Rong","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan 430068, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6674-692X","authenticated-orcid":false,"given":"Li","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Royal Holloway University of London, Egham TW20 0EX, UK"}]},{"given":"Shang","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212000, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1109\/TKDE.2005.99","article-title":"Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions","volume":"17","author":"Adomavicius","year":"2005","journal-title":"IEEE Trans. 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