Title: WebPersonalizer: A Server-Side Recommender System Based on Web Usage Mining
Authors: Bamshad Mobasher
Abstract: Existing approaches to Web personalization often rely heavily on
explicit and subjective user input resulting in static profiles which
are prone to biases. In this paper we present a usage-based Web
personalization system, called WebPersonalizer, drawing heavily upon
Web mining techniques, making the personalization process automatic,
and dynamic. The system architecture separates the offline tasks of
data preparation and Web usage mining, and the online recommendation
engine. At the heart of the system is a technique based on clustering
of user transactions which allows for the discovery of effective
aggregate usage profiles. We discuss how the discovered aggregate
profiles can be used in conjunction with the current status of an
ongoing Web activity to perform real-time personalization. The Web
usage mining approach allows a site to provide effective
personalization using anonymous and implicit user behavioral patterns
without relying on subjective or personally identifying user input.
Keywords: Data Mining, Personalization, Web Usage Mining, Clustering
Full Paper:  [pdf]