Mitigating Position Bias with Regularization for Recommender Systems
Authors: Hao Wang
Abstract: 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.
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