C4.5: programs for machine learning
C4.5: programs for machine learning
Information filtering based on user behavior analysis and best match text retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Characterizing browsing strategies in the World-Wide Web
Proceedings of the Third International World-Wide Web conference on Technology, tools and applications
Personal ontologies for web navigation
Proceedings of the ninth international conference on Information and knowledge management
PVA: a self-adaptive personal view agent system
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Off the beaten tracks: exploring three aspects of web navigation
Proceedings of the 15th international conference on World Wide Web
Behavior based web page evaluation
Proceedings of the 16th international conference on World Wide Web
Investigating User Browsing Behavior
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
A Dynamic Rearrangement Mechanism of Web Page Layouts Using Web Agents
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
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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.