Exploration of consumer preference based on deep learning neural network model in the immersive marketing environment.
The study intends to increase the marketing quantity of various commodities and promote the comprehensive development of the market. The study first discusses the principle and current situation of the emerging Immersive Marketing. Then, it analyzes the Deep Learning (DL) Neural Network (NN) model....
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0268007 |
| _version_ | 1828746464652689408 |
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| author | Qiang Zheng Qingshan Ding |
| author_facet | Qiang Zheng Qingshan Ding |
| author_sort | Qiang Zheng |
| collection | DOAJ |
| description | The study intends to increase the marketing quantity of various commodities and promote the comprehensive development of the market. The study first discusses the principle and current situation of the emerging Immersive Marketing. Then, it analyzes the Deep Learning (DL) Neural Network (NN) model. Finally, a Personalized Recommendation System (PRS) is designed based on the Immersive Marketing environment using the Graph Neural Network (GNN) model. The proposed PRS based on the Immersive Graph Neural Network (IGNN) model has reflected higher advantages over other recommendation systems. The experiment results suggest that Immersive Marketing can fully reflect commodities' essential attributes and characteristics, improve users' shopping experience, and promote sales. Meanwhile, the IGNN-based PRS reported here gives users an elevated and immersive shopping experience and entertainment process. Lastly, the model comparison finds that the proposed IGNN outperforms other models. The optimal model parameters are verified as P@20 and R@20 to gain the highest composite index values. In particular, parameter R@20 gives the model a better performance over P@20. The study provides technical references for improving the marketing process of various commodities and entertainment products and contributes to marketing technology development. |
| first_indexed | 2024-04-14T04:25:06Z |
| format | Article |
| id | doaj.art-5103aa3e5c1f416f92aef6e028d37cf6 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-14T04:25:06Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-5103aa3e5c1f416f92aef6e028d37cf62022-12-22T02:12:20ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01175e026800710.1371/journal.pone.0268007Exploration of consumer preference based on deep learning neural network model in the immersive marketing environment.Qiang ZhengQingshan DingThe study intends to increase the marketing quantity of various commodities and promote the comprehensive development of the market. The study first discusses the principle and current situation of the emerging Immersive Marketing. Then, it analyzes the Deep Learning (DL) Neural Network (NN) model. Finally, a Personalized Recommendation System (PRS) is designed based on the Immersive Marketing environment using the Graph Neural Network (GNN) model. The proposed PRS based on the Immersive Graph Neural Network (IGNN) model has reflected higher advantages over other recommendation systems. The experiment results suggest that Immersive Marketing can fully reflect commodities' essential attributes and characteristics, improve users' shopping experience, and promote sales. Meanwhile, the IGNN-based PRS reported here gives users an elevated and immersive shopping experience and entertainment process. Lastly, the model comparison finds that the proposed IGNN outperforms other models. The optimal model parameters are verified as P@20 and R@20 to gain the highest composite index values. In particular, parameter R@20 gives the model a better performance over P@20. The study provides technical references for improving the marketing process of various commodities and entertainment products and contributes to marketing technology development.https://doi.org/10.1371/journal.pone.0268007 |
| spellingShingle | Qiang Zheng Qingshan Ding Exploration of consumer preference based on deep learning neural network model in the immersive marketing environment. PLoS ONE |
| title | Exploration of consumer preference based on deep learning neural network model in the immersive marketing environment. |
| title_full | Exploration of consumer preference based on deep learning neural network model in the immersive marketing environment. |
| title_fullStr | Exploration of consumer preference based on deep learning neural network model in the immersive marketing environment. |
| title_full_unstemmed | Exploration of consumer preference based on deep learning neural network model in the immersive marketing environment. |
| title_short | Exploration of consumer preference based on deep learning neural network model in the immersive marketing environment. |
| title_sort | exploration of consumer preference based on deep learning neural network model in the immersive marketing environment |
| url | https://doi.org/10.1371/journal.pone.0268007 |
| work_keys_str_mv | AT qiangzheng explorationofconsumerpreferencebasedondeeplearningneuralnetworkmodelintheimmersivemarketingenvironment AT qingshanding explorationofconsumerpreferencebasedondeeplearningneuralnetworkmodelintheimmersivemarketingenvironment |