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International Journal of Crowd Science 2019, 3(3): 267-283 https://doi.org/10.1108/IJCS-07-2019-0018
Research paper | Open Access | Issue | Published: 29 October 2019

Transaction credit in the unstructured crowd transaction network

Show Author's Information Zhishuo Liu1,  Tian Fang2( ),  Yao Dongxin1,  Nianci Kou1
School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China
Department of Business Administration, Pepperdine University, Los Angeles, California, USA
Keywords:
Crowd intelligence, Crowd transaction network, Transaction credit model
Cite this article:
Liu Z, Fang T, Dongxin Y, et al. Transaction credit in the unstructured crowd transaction network. International Journal of Crowd Science, 2019, 3(3): 267-283. https://doi.org/10.1108/IJCS-07-2019-0018
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Abstract Full text About this article
Purpose

Current models of transaction credit in the e-commerce network face many problems, such as the one-sided measurement, low accuracy and insufficient anti-aggression solutions. This paper aims to address these problems by studying the transaction credit problem in the crowd transaction network.

Design/methodology/approach

This study divides the transaction credit into two parts, direct transaction credit and recommended transaction credit, and it proposes a model based on the crowd transaction network. The direct transaction credit comprehensively includes various factors influencing the transaction credit, including transaction evaluation, transaction time, transaction status, transaction amount and transaction times. The recommendation transaction credit introduces two types of recommendation nodes and constructs the recommendation credibility for each type. This paper also proposes a “buyer + circle of friends” method to store and update the transaction credit data.

Findings

The simulation results show that this model is superior with high accuracy and anti-aggression.

Originality/value

The direct transaction credit improves the accuracy of the transaction credit data. The recommendation transaction credit strengthens the anti-aggression of the transaction credit data. In addition, the “buyer + circle of friends” method fully uses the computing of the storage ability of the internet, and it also solves the failure problem of using a single node.


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About this article

Transaction credit in the unstructured crowd transaction network

Show Author's information Zhishuo Liu1, Tian Fang2( ), Yao Dongxin1, Nianci Kou1
School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China
Department of Business Administration, Pepperdine University, Los Angeles, California, USA

Abstract

Purpose

Current models of transaction credit in the e-commerce network face many problems, such as the one-sided measurement, low accuracy and insufficient anti-aggression solutions. This paper aims to address these problems by studying the transaction credit problem in the crowd transaction network.

Design/methodology/approach

This study divides the transaction credit into two parts, direct transaction credit and recommended transaction credit, and it proposes a model based on the crowd transaction network. The direct transaction credit comprehensively includes various factors influencing the transaction credit, including transaction evaluation, transaction time, transaction status, transaction amount and transaction times. The recommendation transaction credit introduces two types of recommendation nodes and constructs the recommendation credibility for each type. This paper also proposes a “buyer + circle of friends” method to store and update the transaction credit data.

Findings

The simulation results show that this model is superior with high accuracy and anti-aggression.

Originality/value

The direct transaction credit improves the accuracy of the transaction credit data. The recommendation transaction credit strengthens the anti-aggression of the transaction credit data. In addition, the “buyer + circle of friends” method fully uses the computing of the storage ability of the internet, and it also solves the failure problem of using a single node.

Keywords: Crowd intelligence, Crowd transaction network, Transaction credit model

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Publication history
Copyright
Acknowledgements
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Publication history

Received: 24 July 2019
Accepted: 01 September 2019
Published: 29 October 2019
Issue date: December 2019

Copyright

© The author(s)

Acknowledgements

Acknowledgements

The authors are grateful for the financial support from the National Key Research and Development Program of China (2017YFB1400100).

Rights and permissions

Zhishuo Liu, Tian Fang, Yao Dongxin and Nianci Kou. Published in International Journal of Crowd Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

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