Research on Improving the Community Group Buying Model with Big Data Technology
- DOI
- 10.2991/978-94-6463-056-5_7How to use a DOI?
- Keywords
- Big data technology; community group buying; Internet platform; simulation experiment; model optimization
- Abstract
In recent years, community group buying has developed rapidly and competition in the market is fierce. The government's restrictions on corporate price wars have caused the development of community group buying platforms to stagnate, and there is an urgent need for reasonable optimization of the model. In the Internet trading platform, big data technology can play a good role in promoting the transaction and development of the platform, and help the development of the platform. This article mainly analyzes the current model of community group buying and its drawbacks, and explores the application methods of big data technology from the perspective of the target market, the supply chain, and the personalized demand. And it optimizes the mode of setting self-pickup points with a weighted algorithm in a simulated situation. Finally, it provides many suggestions for community group buying companies.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Xinjia Zhuang PY - 2022 DA - 2022/12/29 TI - Research on Improving the Community Group Buying Model with Big Data Technology BT - Proceedings of the 2022 2nd International Conference on Management Science and Software Engineering (ICMSSE 2022) PB - Atlantis Press SP - 34 EP - 39 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-056-5_7 DO - 10.2991/978-94-6463-056-5_7 ID - Zhuang2022 ER -