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In developing recommendation services for a new digital library called iLumina (www.ilumina-dlib.org), we are faced with several challenges related to the nature of the data we have available. The availability and consistency of data associated with iLumina is likely to be highly variable. Any recommendation strategy we develop must be able to cope with this fact, while also being robust enough to adapt to additional types of data available over time as the digital library develops. In this paper we describe the challenges we are faced with in developing a system that can provide our users with good, consistent recommendations under changing and uncertain conditions.
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