behavior-based web page evaluation
This paper describes our efforts to investigate factors in a user's browsing behavior to help automatically evaluate web pages that the user shows interest in. To evaluate a web page automatically, we have developed a client-side logging/analyzing tool: the GINIS Framework. We do not focus on clicking, scrolling, navigation, or duration of visit alone, but we propose integrating these patterns of interaction to recognize and evaluate a user's 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 site 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 a machine-learning method. Eventually, GINIS will be able to automatically evaluate web pages using these learned rules, after which the evaluation can be utilized in a variety of user profiling.
web browsing behavior automatic profiling information extraction web usage mining web-human interaction