UBC Theses and Dissertations
Web personalization based on association roles finding on both static and dynamic Web data Lu, Minghao
The explosive and continuous growth in the size and use of the World Wide Web is at the basis of the great interest into web usage mining techniques in both research and commercial areas. In particular, the need for predicting the user’s needs in order to improve the usability and user retention of a web site is more than evident and can be addressed by personalization. In this thesis, we introduce a new framework that takes advantage of the sophisticated association rule finding web mining technology on both dynamic user activities over a web site, such as navigational behavior, and static information, such as user profiles and web content. We also provide a novel personalization selection system which allows users to choose the most suitable profile for them in any given period of time. In order to examine the viability of our framework, we incorporate and implement it over a well designed simulation environment. Moreover, our experiment proves that our framework provides an overall better web personalization service in terms of both recommendation accuracy and user satisfaction.
Item Citations and Data
Attribution-NonCommercial-NoDerivatives 4.0 International