分析網路日誌中有意圖、無意圖及缺失之使用者行為;Analyzing Intentional, Unintentional and Missing User Behaviors in Weblogs

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Title:	分析網路日誌中有意圖、無意圖及缺失之使用者行為;Analyzing Intentional, Unintentional and Missing User Behaviors in Weblogs
Authors:	許哲芸;Hsu, Che-Yun
Contributors:	資訊工程學系
Keywords:	點擊流;網路日誌分析;使用者行為分析
Date:	2020-07-20
Issue Date:	2020-09-02 17:43:42 (UTC+8)
Publisher:	國立中央大學
Abstract:	網路日誌(Weblog) 已廣泛的用來代表使用者線上行為，然而我們發現網路日誌只記錄了使用者部分的行為，例如只記錄到使用者點擊網頁之行為卻忽略網頁分頁間切換之行為。同時可能多紀錄了非使用者自發性的行為，例如當瀏覽的網頁進行重新網址導向或者彈出廣告視窗，後面所開啟的網頁並非使用者意圖想要瀏覽的，但卻會被包含在瀏覽紀錄中。我們發現一般的網路日誌中僅記錄到使用者一半左右的瀏覽行為而且其中5.6% 是屬於使用者可能無意識的行為。透過建立Google Chrome 瀏覽器中plugin 和招募受試者下載使用，我們對有意圖的瀏覽紀錄、無意圖的瀏覽紀錄以及缺失的瀏覽紀錄進行統計，並且發現傳統的瀏覽紀錄中最常使用的網站類型之排名和加上缺失的瀏覽紀錄或者去掉無意圖的瀏覽紀錄之排名是不一樣的，也因此我們對於傳統的瀏覽紀錄是否能代表使用者瀏覽行為產生疑問，依傳統瀏覽記錄進行的分析也可能因此而產生偏誤。本文透過對傳統的瀏覽紀錄、有意圖的瀏覽紀錄及有意圖加上缺失的瀏覽紀錄三者進行分析，並使用常見的分類模型對「下次點擊的事件類型」、「下次點擊會間隔多久」及「未來的瀏覽之網站比例」進行預測，發現相較於傳統的瀏覽紀錄另外兩者皆有良好的表現。這表示網路日誌漏記的使用者行為可能含有額外的資訊且非使用者自發性但存在於網路日記中的紀錄可能雜訊大於資訊。;Weblogs have been widely used to represent the behavior of online users. However, we found that weblog only records part of users’ behaviors. For example, traditional weblogs do not record tab switching and browser window switching. Besides, weblog may record some visits that do not come from a users’ conscious actions. For instance, web pages resulted from page redirects and page pop-ups are recorded in the browsing history, but users may not have intentions to visit these pages. We discover that, on average, weblogs approximately record only half of a users’ page visits and 5.6% of the visits recorded in the weblog belongs to users’ unconscious actions. To collect and analyze the conscious visits, unconscious visits, and ”missing”visits (i.e., the visits that are unrecorded in the traditional weblog), we created a Google Chrome plugin and recruited users to install the plugin. We reported the statistics of visits and showed that sorting the popular website categories based on the traditional weblog is different from the rankings obtained from including the missing visits or excluding the unintentional visits. Therefore, traditional weblog may be a biased representation of a user’s online behaviors, and the observations or conclusions derived from weblog analysis are questionable. Additionally, we predicted users’ future behaviors based on three types of training data –all the visits in traditional weblogs, intentional visits in weblogs, and intentional visits plus missing visits in weblogs. We applied supervised learning algorithms to make predictions. The experiment results show that using intentional visits in weblogs or intentional visits plus missing visits in weblogs usually perform better compared to using all the visits in traditional weblogs. This result indicates that missing visits in weblogs may contain additional information, and unintentional visits in weblogs may have more noise than information.
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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