Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
The advent of the Internet economy era has led to a surge of interest in the efficient management of e-commerce platforms. However, There is a lack of an objective method to evaluate product classification standards, helping policymakers observe their alignment with the market. To address this, we propose the BEML framework, which uses machine learning methods driven by deep learning latent space representations for standard evaluation. Our framework treats the product classification task as a context. It encodes product information using an encoder and simulates classification criteria through a machine learning classifier. Finally, it evaluates the alignment between the product market and classification standards based on the classification efficiency. Through testing 100 kinds of products on the Amazon platform in 2023, our framework evaluates the alignment between the product market and classification standards. The experimental results demonstrate that the BEML framework achieves a macro F1 score of 85.79% and a micro F1 score of 84.73%. Both exceed the current best F1 score by 83.3%, reaching a state-of-the-art level. It provides an efficient and reliable blended learning analysis paradigm for the field of technology and business studies.
Recommended Citation
Liu, Feng and Zheng, Qijian, "An Advanced BERT-Based Commodity Classification on Amazon Online Malls Based on Consumer Cognitive Attributes" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 2.
https://aisel.aisnet.org/hicss-58/ks/digital_transformation/2
An Advanced BERT-Based Commodity Classification on Amazon Online Malls Based on Consumer Cognitive Attributes
Hilton Waikoloa Village, Hawaii
The advent of the Internet economy era has led to a surge of interest in the efficient management of e-commerce platforms. However, There is a lack of an objective method to evaluate product classification standards, helping policymakers observe their alignment with the market. To address this, we propose the BEML framework, which uses machine learning methods driven by deep learning latent space representations for standard evaluation. Our framework treats the product classification task as a context. It encodes product information using an encoder and simulates classification criteria through a machine learning classifier. Finally, it evaluates the alignment between the product market and classification standards based on the classification efficiency. Through testing 100 kinds of products on the Amazon platform in 2023, our framework evaluates the alignment between the product market and classification standards. The experimental results demonstrate that the BEML framework achieves a macro F1 score of 85.79% and a micro F1 score of 84.73%. Both exceed the current best F1 score by 83.3%, reaching a state-of-the-art level. It provides an efficient and reliable blended learning analysis paradigm for the field of technology and business studies.
https://aisel.aisnet.org/hicss-58/ks/digital_transformation/2