  Latest research revealed a considerable lack of reliability within user
feedback and discussed striking impacts for the assessment of adaptive web
systems and content personalisation approaches, e.g. ranking errors, systematic
biases to accuracy metrics as well as its natural offset (the magic barrier).
In order to perform holistic assessments and to improve web systems, a variety
of strategies have been proposed to deal with this so-called human uncertainty.
In this contribution we discuss the most relevant strategies to handle
uncertain feedback and demonstrate that these approaches are more or less
ineffective to fulfil their objectives. In doing so, we consider human
uncertainty within a purely probabilistic framework and utilise hypothesis
testing as well as a generalisation of the magic barrier to compare the effects
of recently proposed algorithms. On this basis we recommend a novel strategy of
acceptance which turns away from mere filtering and discuss potential benefits
for the community of the WWW.
