Personalization with Dynamic Profiler

Abstract

Personalization of Web contents has been widely adopted. It provides users with a more customized experience of a Web site. In this paper, we describe a prototype system, called Dynamic Profiler, that generates dynamic user profiles for personalization. The system can be used in many personalized applications, including targeted advertising, product or content recommendations, and user community services. It uses content-based collaborative filtering techniques to create dynamic user profiles, form user communities and make recommendations. The system analyzes user logs, fetches the documents accessed and categorizes them. Each user is then described by a vector of document categories. Such user characterizations are then used to find user communities based on a projected clustering scheme. The log processing and content categorization are run periodically off-line to capture dynamic user profiles, which are then used online for personalized applications.