Mining User Preferences from Web Access Logs and User Public Information

Laura Thomson, Lecturer, Web Discipline, School of Computer Science and Information Technology, RMIT University, GPO Box 2476V, Melbourne, 3001. Email: laura@cs.rmit.edu.au

Keywords

web mining, data mining, personalized documents

Abstract

Previous studies of web access logs for user profiling and hence document personalization conducted analyses based principally on web site content, usage, and topology. In this work the analysis was conducted on the basis of what could transparently be learned about the end user. The user's IP address was used to obtain publicly available information about the user, specifically their geographical location, top level domain, and the contents of their organizational homepage. This personal information was then combined with standard content and usage analysis. Standard data mining algorithms were applied to web logs from an academic website using a generalization-based clustering feature set with the various types of user data used as the class variable. A number of useful nuggets of information were found, demonstrating the feasibility of this approach.