Commodity Text Classification Based E-commerce Category and Attribute Mining

被引：1

作者：

Zhang, Weijuan ^{[2
]}

Liu, Fan ^{[1
,2
]}

Zhang, Zheqi ^{[3
]}

Liu, Senbin ^{[2
]}

Huang, Qian ^{[2
]}

机构：

[1] Hohai Univ, Minist Educ, Key Lab Coastal Disaster & Def, Nanjing, Peoples R China

[2] Hohai Univ, Coll Comp & Informat, Nanjing, Peoples R China

[3] Calif State Univ Fullerton, Mihaylo Coll Business & Econ, Fullerton, CA 92634 USA

来源：

THIRD INTERNATIONAL CONFERENCE ON MULTIMEDIA INFORMATION PROCESSING AND RETRIEVAL (MIPR 2020) | 2020年

基金：

中国博士后科学基金;

关键词：

component; text classification; category; text similarity; attribute mining; ALGORITHM;

D O I：

10.1109/MIPR49039.2020.00028

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper proposes a commodity text classification-based e-commerce category and attribute mining method. The fastText is utilized to classify the commodity text data and establish the first-level and leaf category mapping between two different e-commerce platforms. Based on the category mapping, the system can mine the attributes and attributes' values that the target leaf category does not have, while its corresponding leaf category from another platform does. Additionally, the longest common subsequence (LCS) algorithm is utilized to calculate the attribute similarity. The experiments on two different e-commerce platforms show that the proposed method not only can help the e-commerce platform establish and improve the category architecture but also enhances user experience.

引用

下载

页码：109 / 112

页数：4

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