Modeling the Browsing Behavior of World Wide Web Users

• 作者: Frederick Kin Hing Phoa Juana Sanchez
• 来源期刊: 统计学期刊(英文)
• 学科: 医学
• 关键词: Power Law Long TAIL Negative BINOMIAL DISTRIBUTION Inverse Gaussian DISTRIBUTION PARETO DISTRIBUTION KOLMOGOROV-SMIRNOV Test
• 年，卷（期）: 2013,（2）
• 页码范围: 145-154
• 页数: 10页
• 分类号: R73

摘要

The World Wide Web is essential to general public nowadays. From a data analysis viewpoint, it provides rich opportunities to gather observational data on a large-scale. This paper focuses on modeling the behavior of visitors to an academic website. Although the conventional probability models, which were used by other literature for fitting in a commercial web site, capture the power law behavior in our data, they fail to capture other important features like the long tail. We propose a new model based on the identities of the users. Qualitative and quantitative tests, which are used for comparing the model fitting to our data, show that the new model outperforms other two conventional probability models.