Abstract
This thesis combines aspects from two approaches to information
access, information filtering and information retrieval, in an effort
to improve the signal to noise ratio in interfaces to conversational
data. These two ideas are blended into one system by augmenting a
search engine indexing Usenet messages with concepts and ideas from
recommender systems theory. My aim is to achieve a situation where
the overall result relevance is improved by exploiting the qualities
of both approaches. Important issues in this context are obtaining
ratings, evaluating relevance rankings and the application of useful
user profiles.
An architecture called NewsView has been designed as part of the work
on this thesis. NewsView describes a framework for interfaces to
Usenet with information retrieval and information filtering concepts
built into it, as well as extensive navigational possibilities within
the data. My aim with this framework is to provide a testbed for user
interface, information filtering and information retrieval issues,
and, most importantly, combinations of the three.