The Reputation Evaluation Method of E-Commerce Merchants Based on Improved Bilstm Network

DOI

10.2991/978-94-6463-570-6_135How to use a DOI?

Keywords

E-commerce; Merchant reputation assessment; Two-way long short-term memory network

Abstract

To address the issue that the semantic and emotional characteristics of consumer reviews are not considered in traditional e-commerce merchant reputation evaluation, this study combines the bidirectional long short-term memory network, the convolutional neural network and the attention mechanism to design a method based on the improved bidirectional long-term memory network. E-commerce merchant reputation evaluation method using short-term memory network. The outcomes indicate that the accuracy of the design method is 4.7%, 10.6%, and 14.9% higher than the bidirectional long short-term memory network, the long short-term memory network, and the convolutional neural network respectively, and the recall rate is 8.6% and 15.5% higher than the other three algorithms, 18.2%. It indicates that the design method can accurately identify the semantic and emotional characteristics of consumer reviews, which proves its effectiveness and is of great significance to the healthy development of e-commerce platforms.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Peng Li
PY  - 2024
DA  - 2024/11/22
TI  - The Reputation Evaluation Method of E-Commerce Merchants Based on Improved Bilstm Network
BT  - Proceedings of the 2024 5th International Conference on Management Science and Engineering Management (ICMSEM 2024)
PB  - Atlantis Press
SP  - 1350
EP  - 1357
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-570-6_135
DO  - 10.2991/978-94-6463-570-6_135
ID  - Li2024
ER  -