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Study on Electronic Commerce Market Based on Customer Behavior Analysis

Author: GaoZuo
Tutor: WangJinLong
School: Qingdao Technological University
Course: Computer Software and Theory
Keywords: Data Mining E-commerce Consumer behavior analysis Human Dynamics Decision Tree Multi - relational mining
CLC: TP393.09
Type: Master's thesis
Year: 2010
Downloads: 289
Quote: 0
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Abstract


With the rapid development of the Internet, through e-commerce website for online shopping activities become increasingly frequent, which prompted service providers need to constantly innovate to meet user demand. As one of the most important information to reflect the user's intent, through the analysis of consumer behavior on the site and mining, to obtain the user's interest and purchase intention to build consumer interest and demand model, targeted change the sales strategy, adjust services to meet customer needs, enhance user stickiness. For further exploration of the e-commerce market, the comprehensive utilization of statistical analysis, analysis, cluster analysis of multi-relational and multi-angle multi-dimensional data processing method to conduct a study on consumer behavior, and help businesses to better understand the user's intent to promote e-commerce innovative services. The main work is as follows: 1) to analyze the behavior of statistical properties to the user's e-commerce market, this paper introduces the the human kinetic analysis method to study the nature of the customer performance statistics on the population level, analysis of customer buying patterns, contrast between different areas of commodity characteristics, as well as the similarities and differences of the behavior patterns of the comments. Through empirical analysis to get some useful findings, the for further characteristic analysis and cluster analysis to provide a basis for. 2) the importance of assessment issues for user behavior characteristics, this paper based on the decision tree induction method of new merchandise buyer behavior research. New merchandise buyers historical transaction data to extract user behavioral characteristics and user attribute characteristics of the goods, to take full advantage of the various acts and attribute characteristics constructing a decision tree to distinguish between the importance of the different characteristics, and further extract useful patterns of behavior. 3) the use of e-commerce widespread multi-relational data, this study calculated based on a unified similarity market segmentation, the introduction of similarity calculation method based on the unified relationship matrix, through the integration of the complex relationship between the e-commerce data, will vary The complex relationship between the configuration object isomorphic get customer / commodity similarity matrix. On this basis, the application of traditional data mining algorithms to cluster analysis, the division of the customer, commodity and extract useful patterns of behavior. Through the study of the above-mentioned problems, the application of e-commerce in China, such as the formulation of marketing strategy, recommended systems technical support, provide good ideas and programs, technology and other related fields of application problems supported.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Computer network > General issues > The application of computer network
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