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Recomm. Syst."],"published-print":{"date-parts":[[2025,9,30]]},"abstract":"<jats:p>\n            This article introduces, to the best of our knowledge, a novel recommendation framework called Cold-start Recommendation based on Knowledge Graph and Meta-learning (CRKM), aimed at enhancing cold-start recommendation performance by addressing the issue of limited user interaction data through the fusion of positive and negative samples. In contrast to other cold-start frameworks, CRKM is divided into three distinct components: the negative sampler, the knowledge graph-based model architecture, and the meta-learner. The negative sampler designed in this article leverages knowledge graphs and popularity information to sample negative labels from items without prior user interaction, thereby mitigating the sparsity of cold-start training data. However, the knowledge graph-based model architecture is responsible for incorporating the nodes and relationships of the knowledge graph into positive and negative samples, using a graph neural network to more effectively learn user and item fusion representations and enhance predictive performance. Finally, the meta-learner performs efficient model initialization parameter updates. We conducted extensive experiments on real-world datasets for cold-start user and item recommendations. CRKM demonstrated notable performance advantages in terms of recall and NDCG when compared to the state-of-the-art methods, thereby validating the rationality and effectiveness of the proposed approach. The source code listing is publicly available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"url\" xlink:href=\"https:\/\/gitee.com\/kyle-liao\/crkm\">https:\/\/gitee.com\/kyle-liao\/crkm<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3654804","type":"journal-article","created":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T13:40:38Z","timestamp":1712065238000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Cold-Start Recommendation based on Knowledge Graph and Meta-Learning under Positive and Negative sampling"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3890-1528","authenticated-orcid":false,"given":"Di","family":"Han","sequence":"first","affiliation":[{"name":"Guangdong University of Finance, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0324-9720","authenticated-orcid":false,"given":"Xiaotian","family":"Jing","sequence":"additional","affiliation":[{"name":"Xi'an Jiaotong University, Xi'an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7840-6145","authenticated-orcid":false,"given":"Yijun","family":"Chen","sequence":"additional","affiliation":[{"name":"Xi'an Aeronautical University, Xi'an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1462-7248","authenticated-orcid":false,"given":"Junmin","family":"Liu","sequence":"additional","affiliation":[{"name":"Xi'an Jiaotong University, Xi'an China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5471-0004","authenticated-orcid":false,"given":"Kai","family":"Liao","sequence":"additional","affiliation":[{"name":"Guangdong University of Finance, Guangzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8081-842X","authenticated-orcid":false,"given":"Wenting","family":"Li","sequence":"additional","affiliation":[{"name":"Guizhou University of Commerce, Guiyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,3,21]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.jocs.2018.09.008","article-title":"HRS-CE: A hybrid framework to integrate content embeddings in recommender systems for cold start items","volume":"29","author":"Anwaar Fahad","year":"2018","unstructured":"Fahad Anwaar, Naima Iltaf, Hammad Afzal, and Raheel Nawaz. 2018. 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