Integrating Web Usage and Content Mining
for More Effective Personalization
Bamshad Mobasher - Hoghua Dai - Tao Luo - Yuqing Sun - Jiang
Zhu
School of Computer Science, Telecommunications, and Information
Systems,
Depaul University, Chicago, Illinois, USA
mobasher@cs.depaul.edu
Abstract:
Recent proposals have suggested Web usage mining as an enabling mechanism
to overcome the problems associated with more traditional Web personalization
techniques such as collaborative or content-based filtering. These problems
include lack of scalability, reliance on subjective user ratings or static
profiles, and the inability to capture a richer set of semantic relationships
among objects (in content-based systems). Yet, usage-based personalization
can be problematic when little usage data is available pertaining to some
objects or when the site content changes regularly. For more effective
personalization, both usage and content attributes of a site must be integrated
into a Web mining framework and used by the recommendation engine in a
uniform manner. In this paper we present such a framework, distinguishing
between the offline tasks of data preparation and mining, and the online
process of customizing Web pages based on a user's active session. We describe
effective techniques based on clustering to obtain a uniform representation
for both site usage and site content profiles, and we show how these profiles
can be used to perform real-time personalization.
