@inproceedings{Billsus:99,
	address = {Seattle},
	title = {A {Personal} {News} {Agent} that {Talks}, {Learns} and {Explains}},
	url = {http://wwwis.win.tue.nl/asum99/billsus.html},
	abstract = {Research on intelligent information agents has
recently attracted much attention. As the amount of
information available online grows with astonishing
speed, people feel overwhelmed navigating through
today's information and media landscape. Information
overload is no longer just a popular buzzword, but a
daily reality for most of us. This leads to a clear
demand for automated methods, commonly referred to as
intelligent information agents, that locate and
retrieve information with respect to users individual
preferences. As intelligent information agents aim to
automatically adapt to individual users, the
development of appropriate user modeling techniques is
of central importance. Algorithms for intelligent
information agents typically draw on work from the
Information Retrieval (IR) and machine learning
communities. Both communities have previously explored
the potential of established algorithms for user
modeling purposes (Belkin et al. 1997; Webb 1998).
However, work in this field is still in its infancy and
we see {\textbackslash}emph\{\vphantom{\}}User Modeling for Intelligent Information
Access\vphantom{\{}\} as an important area for future research.},
	booktitle = {Autonomous {Agents} 99},
	author = {Billsus, Daniel and Pazzani, Michael},
	year = {1999},
	keywords = {Information Agents, human-computer interaction, maschine learning, user modeling},
}

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