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- W4281934021 abstract "This chapter intends to probe into the predictability of consumer behavior classification (CBC) in online virtual stores under the trend of electronic commerce (e-commerce) and provide better consumer services (CS) for online shopping. First, the recurrent neural network (RNN) is expatiated and improved; thereupon, the bidirectional long short-term memory (BiLSTM) algorithm is designed and applied to the CBC; then, the support vector machine (SVM) and naive bayes classifier (NBC) are cited, and a CBC prediction model based on multi-class machine learning (ML) algorithms is implemented. Further, the proposed model is compared with other models from the perspectives of precision, accuracy, F1, and recall; the results signify that the proposed CBC prediction model has presented a 93.95% accuracy, which is at least 4.19% higher than that of other literature algorithms; besides, the performance analysis of network data transmission synchronization reveals that the proposed algorithm outperforms other algorithms with an overall transmission throughput around 1." @default.
- W4281934021 created "2022-06-13" @default.
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- W4281934021 date "2022-06-24" @default.
- W4281934021 modified "2023-10-15" @default.
- W4281934021 title "Consumer Behavior Classification in Online Virtual Stores Using Emotional Intelligence" @default.
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- W4281934021 doi "https://doi.org/10.4018/978-1-6684-4168-8.ch001" @default.
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