Behavior Analysis for Electronic Commerce Trading Systems: A Survey

• Published in: IEEE access Vol. 7; pp. 108703 - 108728
• Main Authors: Zhao, Peihai, Ding, Zhijun, Wang, Mimi, Cao, Ruihao
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Alliances; Analytical models; Authentication; behavior pattern mining; Browsing; Business; Computational modeling; Computer science; Consistency; Correlation analysis; E-commerce trading systems; Electronic commerce; Internet; system behavior; system behavior consistency; User behavior; Wireless networks
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2933247

With the rapid development of the Internet and the continuous progress of computer science and technology, Internet-based online trading has developed rapidly and improved the convenience of everyday life. Increasingly, people conduct business activities through online transactions and payment methods, and the development prospects for online transactions are quite broad. In addition, e-commerce trading systems have attracted considerable attention. Many studies have been conducted to analyze the behaviors of these systems. This paper focuses on analyzing both system behavior and user behavior in e-commerce trading systems. To reflect the characteristics of online trading systems, system behavior reflects the strength of the system, while user behavior reflects various user actions. Consistency, which is a basis for determining whether two objects are similar, provides a reference for designers and modelers. This paper also focuses on analyzing the consistency of system behavior, especially regarding the measurement and diagnosis of e-commerce trading systems. Then, we study the existing user behavior analysis methods for e-commerce trading systems. We divide user behaviors into four groups: web browsing behavior, keystroke behavior, network transaction behavior, and mobile terminal behavior. The performance and correlation analysis techniques of these methods are discussed and compared in detail. Finally, we provide ideas for future research involving behavior analysis for online trading systems.