  Recommendation systems today exert a strong influence on consumer behavior
and individual perceptions of the world. By using collaborative filtering (CF)
methods to create recommendations, it generates a continuous feedback loop in
which user behavior becomes magnified in the algorithmic system. Popular items
get recommended more frequently, creating the bias that affects and alters user
preferences. In order to visualize and compare the different biases, we will
analyze the effects of recommendation systems and quantify the inequalities
resulting from them.
