Personalized Marketing Recommendation System of New Media Short Video Based on Deep Neural Network Data Fusion

With the rapid development of mobile Internet, short video has become another darling after traditional webcast in recent years. How to make full use of short video for effective marketing has become a hot issue that academia and industry are paying close attention to. This article is mainly aimed a...

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Autor principal:	Feifeng Huang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Technology (General) T1-995
Acceso en línea:	https://doaj.org/article/ecb11cdf397a443b9a0356229088b8af
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spelling	oai:doaj.org-article:ecb11cdf397a443b9a0356229088b8af2021-11-29T00:55:37ZPersonalized Marketing Recommendation System of New Media Short Video Based on Deep Neural Network Data Fusion1687-726810.1155/2021/3638071https://doaj.org/article/ecb11cdf397a443b9a0356229088b8af2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/3638071https://doaj.org/toc/1687-7268With the rapid development of mobile Internet, short video has become another darling after traditional webcast in recent years. How to make full use of short video for effective marketing has become a hot issue that academia and industry are paying close attention to. This article is mainly aimed at exploring practical new media through in-depth research and exploration of the specific implementation methods and strategies of short video marketing in social media, based on the advantages and characteristic models of short video marketing in social media. The strategy of short video marketing in social media, and the use of highly in-depth neural network analysis technology for the personalized marketing recommendation system of new media short videos, so as to better promote the use of social media short videos by enterprises or individuals. We have to learn from marketing activities. The experimental results of this article show that when the data volume reaches 80%, the performance of the VRBCH algorithm steadily improves, so the performance of the main F of the VRBCH algorithm is still relatively ideal when the data volume changes. Due to the high dilution of the experimental data set, the amount of data in the VRBCH algorithm has increased sharply by 30% to 35%, but the purchase rate of the marketing recommendation system is as high as 98%. Therefore, the system has high feasibility.Feifeng HuangHindawi LimitedarticleTechnology (General)T1-995ENJournal of Sensors, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology (General) T1-995
spellingShingle	Technology (General) T1-995 Feifeng Huang Personalized Marketing Recommendation System of New Media Short Video Based on Deep Neural Network Data Fusion
description	With the rapid development of mobile Internet, short video has become another darling after traditional webcast in recent years. How to make full use of short video for effective marketing has become a hot issue that academia and industry are paying close attention to. This article is mainly aimed at exploring practical new media through in-depth research and exploration of the specific implementation methods and strategies of short video marketing in social media, based on the advantages and characteristic models of short video marketing in social media. The strategy of short video marketing in social media, and the use of highly in-depth neural network analysis technology for the personalized marketing recommendation system of new media short videos, so as to better promote the use of social media short videos by enterprises or individuals. We have to learn from marketing activities. The experimental results of this article show that when the data volume reaches 80%, the performance of the VRBCH algorithm steadily improves, so the performance of the main F of the VRBCH algorithm is still relatively ideal when the data volume changes. Due to the high dilution of the experimental data set, the amount of data in the VRBCH algorithm has increased sharply by 30% to 35%, but the purchase rate of the marketing recommendation system is as high as 98%. Therefore, the system has high feasibility.
format	article
author	Feifeng Huang
author_facet	Feifeng Huang
author_sort	Feifeng Huang
title	Personalized Marketing Recommendation System of New Media Short Video Based on Deep Neural Network Data Fusion
title_short	Personalized Marketing Recommendation System of New Media Short Video Based on Deep Neural Network Data Fusion
title_full	Personalized Marketing Recommendation System of New Media Short Video Based on Deep Neural Network Data Fusion
title_fullStr	Personalized Marketing Recommendation System of New Media Short Video Based on Deep Neural Network Data Fusion
title_full_unstemmed	Personalized Marketing Recommendation System of New Media Short Video Based on Deep Neural Network Data Fusion
title_sort	personalized marketing recommendation system of new media short video based on deep neural network data fusion
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/ecb11cdf397a443b9a0356229088b8af
work_keys_str_mv	AT feifenghuang personalizedmarketingrecommendationsystemofnewmediashortvideobasedondeepneuralnetworkdatafusion
_version_	1718407806357340160

Personalized Marketing Recommendation System of New Media Short Video Based on Deep Neural Network Data Fusion

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