Can We Find Common Rules of Browsing Behavior?

This paper describes our efforts to investigate factors in user’s browsing behavior to automatically evaluate web pages that the user shows interest in. To evaluate web pages automatically, we developed a client-side logging/analyzing tool: the GINIS Framework. We do not focus on just clicking, scrolling, navigation, or duration of visit alone, but we propose integrating these patterns of interaction to recognize and evaluate user response to a given web page. Unlike most previous web studies that have analyzed access seen at proxies or server, this work focuses primarily on client-side user behavior using a customized web browser. First, GINIS unobtrusively gathers logs of user behavior through the user’s natural interaction with the web browser. Then it analyses the logs and extracts effective rules to evaluate web pages using C4.5 machine learning system. Eventually, GINIS becomes able to automatically evaluate web pages using these learned rules, after which the evaluation can be utilized for a variety of user profiling applications. We successfully confirmed, for example, that time spent on a web page is not the most important factor in predicting interest from behavior, which conflict with the finding of most previous studies.