  Conventional collaborative filtering techniques don't take into consideration
the effect of discrepancy in users' rating perception. Some users may rarely
give 5 stars to items while others almost always assign 5 stars to the chosen
item. Even if they had experience with the same items this systematic
discrepancy in their evaluation style will lead to the systematic errors in the
ability of recommender system to effectively extract right patterns from data.
To mitigate this problem we introduce the ratings' similarity matrix which
represents the dependency between different values of ratings on the population
level. Hence, if on average the correlations between ratings exist, it is
possible to improve the quality of proposed recommendations by off-setting the
effect of either shifted down or shifted up users' rates.
