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Erschienen in: Complex & Intelligent Systems 5/2023

Open Access 08.04.2023 | Correction

Correction to: SimDCL: dropout-based simple graph contrastive learning for recommendation

verfasst von: YuHao Xu, ZhenHai Wang, ZhiRu Wang, YunLong Guo, Rong Fan, HongYu Tian, Xing Wang

Erschienen in: Complex & Intelligent Systems | Ausgabe 5/2023

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The original article can be found online at https://​doi.​org/​10.​1007/​s40747-023-00974-z.

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Correction to: Complex & Intelligent Systems https://doi.org/10.1007/s40747-023-00974-z
In the original publication, the author affiliation has published incorrectly. The correct affiliation is HongYu Tian belongs to “School of Physics and Electronic Engineering” and YuHao Xu, ZhenHai Wang, ZhiRu Wang, YunLong Guo, Rong Fan, Xing Wang belongs to “School of Information Science and Engineering”.
The original article has been corrected.
Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://​creativecommons.​org/​licenses/​by/​4.​0/​.

Publisher's Note

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Titel
Correction to: SimDCL: dropout-based simple graph contrastive learning for recommendation
verfasst von
YuHao Xu
ZhenHai Wang
ZhiRu Wang
YunLong Guo
Rong Fan
HongYu Tian
Xing Wang
Publikationsdatum
08.04.2023
Verlag
Springer International Publishing
Erschienen in
Complex & Intelligent Systems / Ausgabe 5/2023
Print ISSN: 2199-4536
Elektronische ISSN: 2198-6053
DOI
https://doi.org/10.1007/s40747-023-01051-1

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