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Personalized recommendation model of electronic commerce in new media era based on semantic emotion analysis

Electronic commerce (E-commerce) through digital platforms relies on diverse user features to provide a better user experience. In particular, the user experience and connection between digital platforms are exploited through semantic emotions. This provides a personalized recommendation for differe...

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Detalles Bibliográficos
Autores principales: Liu, Yuzhi, Ding, Zhong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9354824/
https://www.ncbi.nlm.nih.gov/pubmed/35936239
http://dx.doi.org/10.3389/fpsyg.2022.952622
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author Liu, Yuzhi
Ding, Zhong
author_facet Liu, Yuzhi
Ding, Zhong
author_sort Liu, Yuzhi
collection PubMed
description Electronic commerce (E-commerce) through digital platforms relies on diverse user features to provide a better user experience. In particular, the user experience and connection between digital platforms are exploited through semantic emotions. This provides a personalized recommendation for different user categories across the E-commerce platforms. This manuscript introduces a Syntactic Data Inquiring Scheme (SDIS) to strengthen the semantic analysis. This scheme first identifies the emotional data based on user comments and repetition on the E-commerce platform. The identifiable and non-identifiable emotion data is classified using positive and repeated comments using the deep learning paradigm. This classification attunes the recommendation system for providing best-affordable user services through product selection, ease of access, promotions, etc. The proposed scheme strengthens the user relationship with the E-commerce platforms by improving the prioritization of user requirements. The user’s interest and recommendation factors are classified and trained for further promotions/recommendations in the learning process. The recommendation data classified from the learning process is used to train and improve the user-platform relationship. The proposed scheme’s performance is analyzed through appropriate experimental considerations. From the experimental analysis, as the session frequency increases, the proposed SDIS maximizes recommendation by 15.1%, the data analysis ratio by 9.41%, and reduces the modification rate by 17%.
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spelling pubmed-93548242022-08-06 Personalized recommendation model of electronic commerce in new media era based on semantic emotion analysis Liu, Yuzhi Ding, Zhong Front Psychol Psychology Electronic commerce (E-commerce) through digital platforms relies on diverse user features to provide a better user experience. In particular, the user experience and connection between digital platforms are exploited through semantic emotions. This provides a personalized recommendation for different user categories across the E-commerce platforms. This manuscript introduces a Syntactic Data Inquiring Scheme (SDIS) to strengthen the semantic analysis. This scheme first identifies the emotional data based on user comments and repetition on the E-commerce platform. The identifiable and non-identifiable emotion data is classified using positive and repeated comments using the deep learning paradigm. This classification attunes the recommendation system for providing best-affordable user services through product selection, ease of access, promotions, etc. The proposed scheme strengthens the user relationship with the E-commerce platforms by improving the prioritization of user requirements. The user’s interest and recommendation factors are classified and trained for further promotions/recommendations in the learning process. The recommendation data classified from the learning process is used to train and improve the user-platform relationship. The proposed scheme’s performance is analyzed through appropriate experimental considerations. From the experimental analysis, as the session frequency increases, the proposed SDIS maximizes recommendation by 15.1%, the data analysis ratio by 9.41%, and reduces the modification rate by 17%. Frontiers Media S.A. 2022-07-22 /pmc/articles/PMC9354824/ /pubmed/35936239 http://dx.doi.org/10.3389/fpsyg.2022.952622 Text en Copyright © 2022 Liu and Ding. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Liu, Yuzhi
Ding, Zhong
Personalized recommendation model of electronic commerce in new media era based on semantic emotion analysis
title Personalized recommendation model of electronic commerce in new media era based on semantic emotion analysis
title_full Personalized recommendation model of electronic commerce in new media era based on semantic emotion analysis
title_fullStr Personalized recommendation model of electronic commerce in new media era based on semantic emotion analysis
title_full_unstemmed Personalized recommendation model of electronic commerce in new media era based on semantic emotion analysis
title_short Personalized recommendation model of electronic commerce in new media era based on semantic emotion analysis
title_sort personalized recommendation model of electronic commerce in new media era based on semantic emotion analysis
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9354824/
https://www.ncbi.nlm.nih.gov/pubmed/35936239
http://dx.doi.org/10.3389/fpsyg.2022.952622
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