Flexible User Profiles for Large Scale Data Delivery

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Date
1999-03-30Author
Cetintemel, Ugur
Franklin, Michael J.
Giles, C. Lee
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Show full item recordAbstract
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