E-commerce merchant classification using website information

Galuh Tunggadewi Sahid, Rahmad Mahendra, Indra Budi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

With the rapid growth of the e-commerce landscape, classifying e-commerce merchants has become an important task as it is an integral part of various processes in e-commerce. One of the examples is merchant on boarding, where the category of an e-commerce merchant has proven to be a good indicator of the risk of the merchant. However, since most of e-commerce businesses do not have brick-and-mortar stores from which we can assess it directly, the only source of information regarding the merchant itself is its website. Thus, we can view this problem as a web classification problem, where we classify e-commerce websites into a category. In this research, we aim to build an end-to-end classification system for e-commerce websites. There are a few challenges such as the number of pages to be processed, imbalanced dataset, and the language of e-commerce websites that can be mixed language. We built a website classification system and experimented with case study of Indonesian and English e-commerce webs, that are classified into 37 different categories. Our best result achieved an F-score of 0.83.

Original languageEnglish
Title of host publicationProceedings of the 9th International Conference on Web Intelligence, Mining and Semantics, WIMS 2019
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450361903
DOIs
Publication statusPublished - 26 Jun 2019
Event9th International Conference on Web Intelligence, Mining and Semantics, WIMS 2019 - Seoul, Korea, Republic of
Duration: 26 Jun 201928 Jun 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference9th International Conference on Web Intelligence, Mining and Semantics, WIMS 2019
Country/TerritoryKorea, Republic of
CitySeoul
Period26/06/1928/06/19

Keywords

  • Classification
  • E-commerce
  • Text processing
  • Web mining

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