Users that populate ratings databases, such as IMDB, follow different
marking practices, in the sense that some are stricter, while others are
more lenient. This aspect has been captured by the most widely used
similarity metrics in collaborative filtering, namely the Pearson
Correlation and the Adjusted Cosine Similarity, which adjust each
individual rating by the mean of the ratings entered by the specific
user, when computing similarities. However, relying on the mean value
presumes that the users' marking practices remain constant over time; in
practice though, it is possible that a user's marking practices change
over time, i.e. a user could start as strict and subsequently become
lenient, or vice versa. In this work, we propose an approach to take
into account marking practices shifts by (1) introducing the concept of
dynamic user rating averages which follow the users' marking practices
shifts, (2) presenting two alternative algorithms for computing a user's
dynamic averages and (3) performing a comparative evaluation among these
two algorithms and the classic static average (unique mean value) that
the Pearson Correlation uses