Commodity classification based on feature enhancement

Yige Fang; Chunmei Ma; Donghao Wu; Tao Zhang

doi:10.1117/12.2682469

26 May 2023 Commodity classification based on feature enhancement

Yige Fang, Chunmei Ma, Donghao Wu, Tao Zhang

Proceedings Volume 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023); 127001T (2023) https://doi.org/10.1117/12.2682469
Event: International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 2023, Nanchang, China

Abstract

The automatic commodity classification system as an important part of e-commerce platform is getting more and more attention. In this paper, we take electronic commodity text title as the research object, deeply analyze its text characteristics, and propose the commodity classification model Bert-VAE with enhanced commodity text title features. Firstly, to address the problem that the current short text classification methods cannot take into account both global and local information on the acquisition of information by word vectors, the Bert model is used to obtain text feature representations. Then, the text feature enhancement method based on Variational Auto-Encoder (VAE) is proposed to address the problems of poor discriminative text titles and unbalanced sample sizes of various categories of commodities. Finally, deep text features and enhanced features are fused to predict commodity categories. In order to verify the model performance, experiments are conducted on the e-tailing commodity dataset, and the experimental results show that the commodity classification model proposed in this paper outperforms other current models in this task to a certain extent.

Citation Download Citation

Yige Fang, Chunmei Ma, Donghao Wu, and Tao Zhang "Commodity classification based on feature enhancement", Proc. SPIE 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 127001T (26 May 2023); https://doi.org/10.1117/12.2682469

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