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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Psychology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyg.2022.952622/full |
| _version_ | 1817969833908633600 |
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| author | Yuzhi Liu Zhong Ding |
| author_facet | Yuzhi Liu Zhong Ding |
| author_sort | Yuzhi Liu |
| collection | DOAJ |
| 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%. |
| first_indexed | 2024-04-13T20:27:13Z |
| format | Article |
| id | doaj.art-ef1cd7e52a204fc0a0355ecce347e10c |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-04-13T20:27:13Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-ef1cd7e52a204fc0a0355ecce347e10c2022-12-22T02:31:19ZengFrontiers Media S.A.Frontiers in Psychology1664-10782022-07-011310.3389/fpsyg.2022.952622952622Personalized recommendation model of electronic commerce in new media era based on semantic emotion analysisYuzhi LiuZhong DingElectronic 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%.https://www.frontiersin.org/articles/10.3389/fpsyg.2022.952622/fullclassified recommendationdeep learningE-commerceemotion analysis classified recommendationemotion analysis |
| spellingShingle | Yuzhi Liu Zhong Ding Personalized recommendation model of electronic commerce in new media era based on semantic emotion analysis Frontiers in Psychology classified recommendation deep learning E-commerce emotion analysis classified recommendation 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 | classified recommendation deep learning E-commerce emotion analysis classified recommendation emotion analysis |
| url | https://www.frontiersin.org/articles/10.3389/fpsyg.2022.952622/full |
| work_keys_str_mv | AT yuzhiliu personalizedrecommendationmodelofelectroniccommerceinnewmediaerabasedonsemanticemotionanalysis AT zhongding personalizedrecommendationmodelofelectroniccommerceinnewmediaerabasedonsemanticemotionanalysis |