Transaction credit in the unstructured crowd transaction network

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

[1]  Liu Yingchun,et al.  Trustworthy services discovery based on trust and recommendation relationships , 2012 .

[2]  Hector Garcia-Molina,et al.  The Eigentrust algorithm for reputation management in P2P networks , 2003, WWW '03.

[3]  Dwayne D. Gremler,et al.  Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the Internet? , 2004 .

[4]  Hong-Youl Ha.  Factors influencing consumer perceptions of brand trust online , 2004 .

[5]  Mario Cloutier,et al.  Six Degrees of Separation Six Degrees of Separation , 1994 .

[6]  Li Jian-Zhong,et al.  A Reputation-Based Trust Mechanism for P2P E-Commerce Systems , 2007 .

[7]  Zou Peng,et al.  A Recommendation-Based Peer-to-Peer Trust Model , 2004 .

[8]  Huang Xue-feng.  Research of RGTrust model in online transactions' credit control , 2007 .

[9]  Karl Aberer,et al.  Managing trust in a peer-2-peer information system , 2001, CIKM '01.

[10]  Dimitrios Gunopulos,et al.  A local search mechanism for peer-to-peer networks , 2002, CIKM '02.

[11]  Jing-Tao Li.  A Trust Model Based on Similarity-Weighted Recommendation for P2P Environments , 2007 .

[12]  Pranjal Gupta,et al.  How e-WOM recommendations influence product consideration and quality of choice: A motivation to process information perspective , 2010 .

[13]  Yong Liu.  Word-of-Mouth for Movies: Its Dynamics and Impact on Box Office Revenue , 2006 .

[14]  Sharon L. Milgram,et al.  The Small World Problem , 1967 .

[15]  Ling Liu,et al.  PeerTrust: supporting reputation-based trust for peer-to-peer electronic communities , 2004, IEEE Transactions on Knowledge and Data Engineering.

[16]  Charles Wang,et al.  Selective Walk Searching Algorithm for Gnutella Network , 2007, 2007 4th IEEE Consumer Communications and Networking Conference.

[17]  Li Guo.  Research on Credit Mechanism in E-commerce Environment , 2017 .