  Fairness is a popular research topic in recent years. A research topic
closely related to fairness is bias and debiasing. Among different types of
bias problems, position bias is one of the most widely encountered symptoms.
Position bias means that recommended items on top of the recommendation list
has a higher likelihood to be clicked than items on bottom of the same list. To
mitigate this problem, we propose to use regularization technique to reduce the
bias effect. In the experiment section, we prove that our method is superior to
other modern algorithms.
