NING Lianju, WANG Haoyu, FENG Xin, DU Junping. The Browsing Pattern and Review Model of Online Consumers Based on Large Data Analysis[J]. Chinese Journal of Electronics, 2015, 24(1): 58-64.
Citation: NING Lianju, WANG Haoyu, FENG Xin, DU Junping. The Browsing Pattern and Review Model of Online Consumers Based on Large Data Analysis[J]. Chinese Journal of Electronics, 2015, 24(1): 58-64.

The Browsing Pattern and Review Model of Online Consumers Based on Large Data Analysis

Funds:  This work is supported by the National Natural Science Foundation of China (No.71271032), National Basic Research Program of China (973 Program) (No.2012CB821200, No.2012CB821206), and National Natural Science Foundation of China (No.61320106006).
  • Received Date: 2013-01-01
  • Rev Recd Date: 2014-09-01
  • Publish Date: 2015-01-10
  • In the context of online shopping, commodity information and consumer reviews are main factors that will affect purchasing behavior. Started from the preference of commodity information browsing and the inherent property of online reviews, this paper focuses on the browsing data for statistical analysis and the interval distribution of consumer reviews based on the real data of 360buy which is the domestic large-scale B2C commerce website in China. Researches find that commodity information browsing time distribution on Internet is fragment and can be depicted by the fat tail effect. It also demonstrated that user's browsing patterns are related to the type of information and the displaying of the information, which means that the pieces of the picture and the length of the titles affecting the clicks rate. Reviews on the interval distribution can be depicted by the power-law function and there is a monotonically increasing relationship between power-exponent and the customers' concerns with the corresponding commodity, the higher the exponent, the higher the degree of consumer attention. The finding obtained some basic rules of the browsing mode and review model, which is of important significance for future research.
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