Flexible User Profiles for Large Scale Data Delivery

Abstract

Push-based data delivery requires knowledge of user interests for making scheduling, bandwidth allocation, and routing decisions. Such information is maintained as user profiles. We propose a new incremental algorithm for constructing user profiles based on monitoring and user feedback. In contrast to earlier approaches, which typically represent profiles as a single weighted interest vector, we represent user-profiles using multiple interest clusters, whose number, size, and elements change adaptively based on user access behavior. This flexible approach allows the profile to more accurately represent complex user interests. The approach can be tuned to trade off profile complexity and effectiveness, making it suitable for use in large-scale information filtering applications such as push-based WWW page dissemination. We evaluate the method by experimentally investigating its ability to categorize WWW pages taken from Yahoo! categories. Our results show that the method can provide high retrieval effectiveness with modest profile sizes and can effectively adapt to changes in users' interests. Also cross-referenced as UMIACS-TR-99-18