Optimized Attention Enhanced Temporal Graph Convolutional Network Espoused Research of Intelligent Customer Service System based on Natural Language Processing Technology
Consumers have begun to move their attention away from product functioning and toward value probably extracted from items. Companies have begun to use customer service systems (CSS) in response to this trend, which are business models that give clients with not solitary tangible items as well as int...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2024.2327867 |
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| _version_ | 1849220190829543424 |
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| author | Zhifeng Wei Hongyan Wang Qiang Xu Yi Qu Wei Xing |
| author_facet | Zhifeng Wei Hongyan Wang Qiang Xu Yi Qu Wei Xing |
| author_sort | Zhifeng Wei |
| collection | DOAJ |
| description | Consumers have begun to move their attention away from product functioning and toward value probably extracted from items. Companies have begun to use customer service systems (CSS) in response to this trend, which are business models that give clients with not solitary tangible items as well as intangible facilities. Even with substantial investigation on Smart CSS frameworks, rare of this frameworks considered customers active data producers actively creating data for the Smart CSS. Furthermore, the majority of them offered a generic remedy rather than a personalized one. To classify customer service systems, performance metrics, such as precision, accuracy, F1-score, Recall (Sensitivity), Specificity, Error rate, Computation time, and RoC are considered. The performance of AETGCN-NGOA-CSS approach attains 19.11%, 24.12% and 28.13% high specificity, 24.93%, 23.04%, and 9.51% lower computation time, 15.2%, 25.45% and 13.91% higher ROC and 8.45%, 20.98%, and 27.55% higher accuracy compared with existing methods, such as developing personalized recommendation system in smart product service system depend on unsupervised learning model (CSS-BERT), Cognitive Decision-Making approaches in Data-driven Retail Intelligence: Consumer Sentiments, Choices, Shopping Behaviors (CSS-CDMA), e-Commerce Online Intelligent Customer Service System under Fuzzy Control (CSS-FFNN), respectively. |
| format | Article |
| id | doaj-art-3d9444e6eeec40f2820f904c8f7d187d |
| institution | Kabale University |
| issn | 0883-9514 1087-6545 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj-art-3d9444e6eeec40f2820f904c8f7d187d2024-12-16T16:13:01ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452024-12-0138110.1080/08839514.2024.2327867Optimized Attention Enhanced Temporal Graph Convolutional Network Espoused Research of Intelligent Customer Service System based on Natural Language Processing TechnologyZhifeng Wei0Hongyan Wang1Qiang Xu2Yi Qu3Wei Xing4Beijing Sgitg Accenture Information Technology Center Co.Ltd, Beijing, ChinaBeijing China-Power Information Technology Co.Ltd, Beijing, ChinaBeijing China-Power Information Technology Co.Ltd, Beijing, ChinaState Grid Customer Service Centre, Tianjin, ChinaState Grid Customer Service Centre, Tianjin, ChinaConsumers have begun to move their attention away from product functioning and toward value probably extracted from items. Companies have begun to use customer service systems (CSS) in response to this trend, which are business models that give clients with not solitary tangible items as well as intangible facilities. Even with substantial investigation on Smart CSS frameworks, rare of this frameworks considered customers active data producers actively creating data for the Smart CSS. Furthermore, the majority of them offered a generic remedy rather than a personalized one. To classify customer service systems, performance metrics, such as precision, accuracy, F1-score, Recall (Sensitivity), Specificity, Error rate, Computation time, and RoC are considered. The performance of AETGCN-NGOA-CSS approach attains 19.11%, 24.12% and 28.13% high specificity, 24.93%, 23.04%, and 9.51% lower computation time, 15.2%, 25.45% and 13.91% higher ROC and 8.45%, 20.98%, and 27.55% higher accuracy compared with existing methods, such as developing personalized recommendation system in smart product service system depend on unsupervised learning model (CSS-BERT), Cognitive Decision-Making approaches in Data-driven Retail Intelligence: Consumer Sentiments, Choices, Shopping Behaviors (CSS-CDMA), e-Commerce Online Intelligent Customer Service System under Fuzzy Control (CSS-FFNN), respectively.https://www.tandfonline.com/doi/10.1080/08839514.2024.2327867 |
| spellingShingle | Zhifeng Wei Hongyan Wang Qiang Xu Yi Qu Wei Xing Optimized Attention Enhanced Temporal Graph Convolutional Network Espoused Research of Intelligent Customer Service System based on Natural Language Processing Technology Applied Artificial Intelligence |
| title | Optimized Attention Enhanced Temporal Graph Convolutional Network Espoused Research of Intelligent Customer Service System based on Natural Language Processing Technology |
| title_full | Optimized Attention Enhanced Temporal Graph Convolutional Network Espoused Research of Intelligent Customer Service System based on Natural Language Processing Technology |
| title_fullStr | Optimized Attention Enhanced Temporal Graph Convolutional Network Espoused Research of Intelligent Customer Service System based on Natural Language Processing Technology |
| title_full_unstemmed | Optimized Attention Enhanced Temporal Graph Convolutional Network Espoused Research of Intelligent Customer Service System based on Natural Language Processing Technology |
| title_short | Optimized Attention Enhanced Temporal Graph Convolutional Network Espoused Research of Intelligent Customer Service System based on Natural Language Processing Technology |
| title_sort | optimized attention enhanced temporal graph convolutional network espoused research of intelligent customer service system based on natural language processing technology |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2024.2327867 |
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