  It is well known that explicit user ratings in recommender systems are biased
towards high ratings, and that users differ significantly in their usage of the
rating scale. Implementers usually compensate for these issues through rating
normalization or the inclusion of a user bias term in factorization models.
However, these methods adjust only for the central tendency of users'
distributions. In this work, we demonstrate that lack of \textit{flatness} in
rating distributions is negatively correlated with recommendation performance.
We propose a rating transformation model that compensates for skew in the
rating distribution as well as its central tendency by converting ratings into
percentile values as a pre-processing step before recommendation generation.
This transformation flattens the rating distribution, better compensates for
differences in rating distributions, and improves recommendation performance.
We also show a smoothed version of this transformation designed to yield more
intuitive results for users with very narrow rating distributions. A
comprehensive set of experiments show improved ranking performance for these
percentile transformations with state-of-the-art recommendation algorithms in
four real-world data sets.
