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Research on the Development Model of University Archives Cultural Products Based on Deep Learning.

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Bibliographic Details
Title: Research on the Development Model of University Archives Cultural Products Based on Deep Learning.
Authors: Qiong Luo
Source: Computer Systems Science & Engineering; 2023, Vol. 46 Issue 3, p3141-3158, 18p
Subject Terms: DEEP learning, UNIVERSITY & college archives, MATHEMATICAL optimization, ARTIFICIAL neural networks, MACHINE learning
Abstract: The products of an archival culture in colleges and universities are the final result of the development of archival cultural resources, and the development of archival cultural effects in colleges and universities should be an important part of improving the artistic level of libraries. The existing RippleNet model doesn't consider the influence of key nodes on recommendation results, and the recommendation accuracy is not high. Therefore, based on the RippleNet model, this paper introduces the influence of complex network nodes into the model and puts forward the Cn RippleNet model. The performance of the model is verified by experiments, which provide a theoretical basis for the promotion and recommendation of its cultural products of universarchives, solve the problem that RippleNet doesn't consider the influence of key nodes on recommendation results, and improve the recommendation accuracy. This paper also combs the development course of archival cultural products in detail. Finally, based on the Cn-RippleNet model, the cultural effect of university archives is recommended and popularized. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract:The products of an archival culture in colleges and universities are the final result of the development of archival cultural resources, and the development of archival cultural effects in colleges and universities should be an important part of improving the artistic level of libraries. The existing RippleNet model doesn't consider the influence of key nodes on recommendation results, and the recommendation accuracy is not high. Therefore, based on the RippleNet model, this paper introduces the influence of complex network nodes into the model and puts forward the Cn RippleNet model. The performance of the model is verified by experiments, which provide a theoretical basis for the promotion and recommendation of its cultural products of universarchives, solve the problem that RippleNet doesn't consider the influence of key nodes on recommendation results, and improve the recommendation accuracy. This paper also combs the development course of archival cultural products in detail. Finally, based on the Cn-RippleNet model, the cultural effect of university archives is recommended and popularized. [ABSTRACT FROM AUTHOR]
ISSN:02676192
DOI:10.32604/csse.2023.038017