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International Journal of Crowd Science 2023, 7(3): 131-136 https://doi.org/10.26599/IJCS.2023.9100006
Open Access | Issue | Published: 30 September 2023

Product Map Analysis from a Crowd of Small- and Medium-Sized E-Commerce Sites: A Bottom-Up Approach

Show Author's Information Xin Li1,  Tongda Zhang1,  Xiao Sun2,3,  Yongsheng Ma1( )
Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Department of Automation, Tsinghua University, Beijing 100084, China
National Engineering Laboratory for E-commerce Technologies, Tsinghua University, Beijing 100084, China
Keywords:
bottom-up, crowd science, product map, small- and medium-sized e-commerce sites
Cite this article:
Li X, Zhang T, Sun X, et al. Product Map Analysis from a Crowd of Small- and Medium-Sized E-Commerce Sites: A Bottom-Up Approach. International Journal of Crowd Science, 2023, 7(3): 131-136. https://doi.org/10.26599/IJCS.2023.9100006
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Abstract Full text Electronic supplementary material About this article

The study of product maps in e-commerce has garnered significant attention from academics and practitioners, as they provide insights into the relationship between products, such as complementarity and competition. However, existing studies have focused on the perspectives of large manufacturers and retailers, using data from these central sources. This paper adopts a bottom-up approach based on crowd intelligence, with small- and medium-sized e-commerce (SME) sites serving as independent data providers. This approach allows for the decentralized processing of data and enables the aggregation of diverse perspectives and insights from a large number of independent sources. A graph term frequency-inverse document frequency method is proposed, which can measure the similarities of products and build a product map. The method was employed to find a hierarchical community structure using data from over 90000 products from 52 SME sites. The results showed that products within the same site tend to be distributed across the same community. Our findings can assist e-commerce sites in making informed decisions about pricing and product offerings, leading to more diversified production.


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About this article

Product Map Analysis from a Crowd of Small- and Medium-Sized E-Commerce Sites: A Bottom-Up Approach

Show Author's information Xin Li1, Tongda Zhang1, Xiao Sun2,3, Yongsheng Ma1( )
Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Department of Automation, Tsinghua University, Beijing 100084, China
National Engineering Laboratory for E-commerce Technologies, Tsinghua University, Beijing 100084, China

Abstract

The study of product maps in e-commerce has garnered significant attention from academics and practitioners, as they provide insights into the relationship between products, such as complementarity and competition. However, existing studies have focused on the perspectives of large manufacturers and retailers, using data from these central sources. This paper adopts a bottom-up approach based on crowd intelligence, with small- and medium-sized e-commerce (SME) sites serving as independent data providers. This approach allows for the decentralized processing of data and enables the aggregation of diverse perspectives and insights from a large number of independent sources. A graph term frequency-inverse document frequency method is proposed, which can measure the similarities of products and build a product map. The method was employed to find a hierarchical community structure using data from over 90000 products from 52 SME sites. The results showed that products within the same site tend to be distributed across the same community. Our findings can assist e-commerce sites in making informed decisions about pricing and product offerings, leading to more diversified production.

Keywords: bottom-up, crowd science, product map, small- and medium-sized e-commerce sites

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Publication history

Received: 16 February 2023
Revised: 28 March 2023
Accepted: 03 April 2023
Published: 30 September 2023
Issue date: September 2023

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© The author(s) 2023.

Acknowledgements

Acknowledgment

The authors wish to thank all the respected professors who helped during the experiment, development, and writing phase of this paper.

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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