  In the information age we are living in today, not only are we interested in
accessing multimedia objects such as documents, videos, etc. but also in
searching for professional experts, people or celebrities, possibly for
professional needs or just for fun. Information access systems need to be able
to extract and exploit various sources of information (usually in text format)
about such individuals, and to represent them in a suitable way usually in the
form of a profile. In this article, we tackle the problems of profile-based
expert recommendation and document filtering from a machine learning
perspective by clustering expert textual sources to build profiles and capture
the different hidden topics in which the experts are interested. The experts
will then be represented by means of multi-faceted profiles. Our experiments
show that this is a valid technique to improve the performance of expert
finding and document filtering.
