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.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:BVAU.2429/4162 |
| Date | 11 1900 |
| Creators | Lu, Minghao |
| Publisher | University of British Columbia |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
Page generated in 0.0019 seconds