Journal Home > Volume 7 , Issue 3
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
Download citation
EndNote(RIS)
BibTeX

245

Views

10

Downloads

Citations

0

Crossref

N/A

WoS

0

Scopus

N/A

CSCD

Abstract Full text 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.


menu
Abstract
Full text
Outline
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

References(14)

[1]

T. Elrod, G. J. Russell, A. D. Shocker, R. L. Andrews, L. Bacon, B. L. Bayus, J. D. Carroll, R. M. Johnson, W. A. Kamakura, P. Lenk, et al., Inferring market structure from customer response to competing and complementary products, Mark. Lett., vol. 13, no. 3, pp. 221–232, 2002.
DOI Google Scholar
[2]

R. Venkatesan and P. W. Farris, Measuring and managing returns from retailer-customized coupon campaigns, J. Mark., vol. 76, no. 1, pp. 76–94, 2012.
DOI Google Scholar
[3]
I. K. Mensah and D. S. Mwakapesa, Cross-border e-commerce diffusion and usage during the period of the COVID-19 pandemic: A literature review, in Proc. 3rd Africa-Asia Dialogue Network (AADN) Int. Conf. Advances in Business Management and Electronic Commerce Research, Ganzhou, China, 2021, pp. 59–65.
DOI
[4]

D. Tolstoy, E. R. Nordman, S. M. Hånell, and N. Özbek, The development of international e-commerce in retail SMEs: An effectuation perspective, J. World Bus., vol. 56, no. 3, p. 101165, 2021.
DOI Google Scholar
[5]

S. Gabel, D. Guhl, and D. Klapper, P2V-MAP: Mapping market structures for large retail assortments, Journal of Marketing Research, vol. 56, no. 4, pp. 557–580, 2019.
DOI Google Scholar
[6]

J. B. Kim, P. Albuquerque, and B. J. Bronnenberg, Mapping online consumer search, J. Mark. Res., vol. 48, no. 1, pp. 13–27, 2011.
DOI Google Scholar
[7]

O. Netzer, R. Feldman, J. Goldenberg, and M. Fresko, Mine your own business: Market-structure surveillance through text mining, Mark. Sci., vol. 31, no. 3, pp. 521–543, 2012.
DOI Google Scholar
[8]

C. I. Eke, A. A. Norman, L. Shuib, and H. F. Nweke, A survey of user profiling: State-of-the-art, challenges, and solutions, IEEE Access, vol. 7, pp. 144907–144924, 2019.
DOI Google Scholar
[9]

S. Qaiser and R. Ali, Text mining: Use of TF-IDF to examine the relevance of words to documents, Int. J. Comput. Appl., vol. 181, no. 1, pp. 25–29, 2018.
DOI Google Scholar
[10]

L. Yao, C. Mao, and Y. Luo, Graph convolutional networks for text classification, Proc. AAAI Conf. Artif. Intell., vol. 33, no. 1, pp. 7370–7377, 2019.
DOI Google Scholar
[11]

C. H. Huang, J. Yin, and F. Hou, A text similarity measurement combining word semantic information with TF-IDF method, Chin. J. Comput., vol. 34, no. 5, pp. 856–864, 2011.
DOI Google Scholar
[12]
S. S. M. M. Rahman, K. B. M. B. Biplob, M. H. Rahman, K. Sarker, and T. Islam, An investigation and evaluation of N-Gram, TF-IDF and ensemble methods in sentiment classification, in Proc. Second EAI International Conference on Cyber Security and Computer Science (ICONCS 2020), Dhaka, Bangladesh, 2020, pp. 391–402.
DOI
[13]

V. D. Blondel, J. L. Guillaume, R. Lambiotte, and E. Lefebvre, Fast unfolding of communities in large networks, J. Stat. Mech., vol. 2008, no. 10, p. P10008, 2008.
DOI Google Scholar
[14]
T. Zhang, J. Qian, X. Sun, Y. Yuan, and M. Chen, A glance at people’s engagement behavior with fully pay transparency: From the perspective of a crowd collaboration system, in Proc. 5th Int. Conf. Crowd Science and Engineering, Jinan, China, 2021, pp. 1–7.
DOI
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

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

Copyright

© 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.

Rights and permissions

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/).

Return