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| Identifying Commodity Names Based on XGBoost Model |
Xiaofeng Li1,Jing Ma1( ),Chi Li2,Hengmin Zhu3 |
1(College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)
2(Alibaba Zhejiang Rookie Supply Chain Management Co., Ltd. , Hangzhou 311100, China)
3(College of Economics and Management, Nanjing University of Posts and Telecommunications, Nanjing 210046, China) |
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Abstract [Objective] This paper tries to automatically identify commodity names from product descriptions, aiming to classifying items sold by Taobao. [Methods] First, we retrieved a large number of transaction records from Taobao. Then, we built an e-commerce commodity description dataset and labeled it manually. Third, we created a supervised machine learning algorithm based on the XGBoost model to extract names from product description. [Results] The precision and recall of the algorithm was 85% and 87% for 816 different items from 20,059 records. [Limitations] Categories of commodities in the test corpus need to be expanded. [Conclusions] Machine learning algorithm is an effective way to identify product names.
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Received: 20 September 2018
Published: 06 September 2019
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Corresponding Authors:
Jing Ma
E-mail: majing5525@126.com
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