  This paper studies user attributes in light of current concerns in the
recommender system community: diversity, coverage, calibration, and data
minimization. In experiments with a conventional context-aware recommender
system that leverages side information, we show that user attributes do not
always improve recommendation. Then, we demonstrate that user attributes can
negatively impact diversity and coverage. Finally, we investigate the amount of
information about users that ``survives'' from the training data into the
recommendation lists produced by the recommender. This information is a weak
signal that could in the future be exploited for calibration or studied further
as a privacy leak.
