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In the information age we are living in today, not only are we interested inaccessing multimedia objects such as documents, videos, etc. but also insearching for professional experts, people or celebrities, possibly forprofessional needs or just for fun. Information access systems need to be ableto extract and exploit various sources of information (usually in text format)about such individuals, and to represent them in a suitable way usually in theform of a profile. In this article, we tackle the problems of profile-basedexpert recommendation and document filtering from a machine learningperspective by clustering expert textual sources to build profiles and capturethe different hidden topics in which the experts are interested. The expertswill then be represented by means of multi-faceted profiles. Our experimentsshow that this is a valid technique to improve the performance of expertfinding and document filtering.

Automatic Construction of Multi-faceted User Profiles using Text Clustering and its Application to Expert Recommendation and Filtering Problems