经验:分析客户端Web日志中缺失的网页访问和非故意的网页访问

Che-Yun Hsu, Ting-Rui Chen, Hung-Hsuan Chen
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引用次数: 1

摘要

Web日志被广泛用于记录在线用户的网页访问情况。然而,我们发现Chrome浏览器的浏览历史记录只记录了57%的用户访问过的网站,也就是说,将近一半的用户网站访问没有被记录。此外,5.1%记录在网络日志中的访问是由于无意识的用户行为,也就是说,这些页面访问不是由用户发起的。我们创建了一个谷歌Chrome插件,并招募用户安装该插件来收集和分析有意识的URL访问、无意识的URL访问和“丢失”的URL访问(即传统web日志中未记录的访问)。我们报告了这些行为的统计数据。我们表明,基于传统网络日志对热门网站类别进行排序,不同于包括未访问或排除非故意访问时获得的排名。我们基于三种类型的训练数据——现代网络日志中的所有访问、网络日志中的有意访问和网络日志中的有意访问加未访问,来预测用户未来的行为。实验结果表明,web日志中缺失的访问可能包含额外的信息,而web日志中无意的访问可能包含比用户建模信息更多的噪声。因此,我们需要小心从网络日志分析中得出的观察和结论,因为网络日志数据可能是用户访问过的网页的不完整和嘈杂的数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Experience: Analyzing Missing Web Page Visits and Unintentional Web Page Visits from the Client-side Web Logs
Web logs have been widely used to represent the web page visits of online users. However, we found that web logs in Chrome’s browsing history only record 57% of users’ visited websites, i.e., nearly half of a user’s website visits are not recorded. Additionally, 5.1% of the visits recorded in the web log occur because of unconscious user actions, i.e., these page visits are not initiated from users. We created a Google Chrome plugin and recruited users to install the plugin to collect and analyze the conscious URL visits, unconscious URL visits, and “missing” URL visits (i.e., the visits unrecorded in the traditional web log). We reported the statistics of these behaviors. We showed that sorting popular website categories based on traditional web logs differs from the rankings obtained when including missing visits or excluding unintentional visits. We predicted users’ future behaviors based on three types of training data – all the visits in modern web logs, the intentional visits in web logs, and the intentional visits plus missing visits in web logs. The experimental results indicate that missing visits in web logs may contain additional information, and unintentional visits in web logs may contain more noise than information for user modeling. Consequently, we need to be careful of the observations and conclusions derived from web log analyses because the web log data could be an incomplete and noisy dataset of a user’s visited web pages.
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