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Knowl. Discov. Data"],"published-print":{"date-parts":[[2026,6,30]]},"abstract":"<jats:p>\n                    Recommender systems are effective tools to alleviate the challenges posed by information overload, but data sparsity has greatly affected their performance.\n                    <jats:bold>Knowledge Graphs (KGs)<\/jats:bold>\n                    and self-supervised learning are used to alleviate the data sparsity problem. However, existing KG-enhanced self-supervised learning recommendation methods have the following limitations: (1)\n                    <jats:italic toggle=\"yes\">Generality<\/jats:italic>\n                    : existing CL-based recommendation methods strongly rely on manually designed data augmentation strategies, leading to poor generality of the CL-based models. (2)\n                    <jats:italic toggle=\"yes\">Robustness<\/jats:italic>\n                    : KG usually contains lots of task-irrelevant entities, and the user\u2013item interactions constructed from implicit feedback are usually noisy. The noisy data will generate intrusive supervised and self-supervised signals and will degrade recommendation performance. To address these limitations, we propose a novel KG-enhanced self-supervised learning recommendation method, named\n                    <jats:bold>Knowledge-Variational Contrastive Learning for Recommendation (KVCL)<\/jats:bold>\n                    . Specifically, we first design an adaptively denoising mechanism to identify and prune the noisy data in the KG and user\u2013item interaction bipartite graph. Then, we learn a normal distribution for each node by the variational auto-encoder, and sample multiple times from the learned distribution to obtain different contrastive views. Extensive experiments based on three public datasets show that KVCL achieves improved performance over state-of-the-art methods, notably with 3.13% performance gain over state-of-the-art methods on Recall@20 and NDCG@20. Furthermore, evaluations including ablation studies and detailed analyses of multi-scenarios, computational efficiency, complexity, and denoising interpretability further underscore its scalability and practical applicability.\n                  <\/jats:p>","DOI":"10.1145\/3805046","type":"journal-article","created":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T14:49:27Z","timestamp":1774968567000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Knowledge-Variational Contrastive Learning for Recommendation"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3318-9004","authenticated-orcid":false,"given":"Zhiqiang","family":"Huang","sequence":"first","affiliation":[{"name":"The Ministry of Education Key Lab for Intelligent Networks and Network Security, School of Computer Science and Technology, Xi\u2019an Jiaotong University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4874-2567","authenticated-orcid":false,"given":"Tao","family":"Qin","sequence":"additional","affiliation":[{"name":"The Ministry of Education Key Lab for Intelligent Networks and Network Security, School of Computer Science and Technology, Xi\u2019an Jiaotong University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7961-5355","authenticated-orcid":false,"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"The Ministry of Education Key Lab for Intelligent Networks and Network Security, School of Computer Science and Technology, Xi\u2019an Jiaotong University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1434-837X","authenticated-orcid":false,"given":"Pinghui","family":"Wang","sequence":"additional","affiliation":[{"name":"The Ministry of Education Key Lab for Intelligent Networks and Network Security, School of Computer Science and Technology, Xi\u2019an Jiaotong University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8261-9380","authenticated-orcid":false,"given":"Kuiyu","family":"Zhu","sequence":"additional","affiliation":[{"name":"The Ministry of Education Key Lab for Intelligent Networks and Network Security, School of Computer Science and Technology, Xi\u2019an Jiaotong University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,5,20]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313705"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645517"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591645"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959190"},{"key":"e_1_3_2_6_2","first-page":"249","volume-title":"Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS \u201910)","author":"Glorot Xavier","year":"2010","unstructured":"Xavier Glorot and Yoshua Bengio. 2010. 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