Bounding System-Induced Biases in Recommender Systems with a Randomized Dataset.

The article focuses on addressing biases in recommender systems caused by system-induced and user-induced factors. It introduces the concept of using a randomized dataset to mitigate system-induced biases and proposes a new theoretical framework to optimize the upper bound of an ideal objective function for debiasing. It also presents a novel method called "debiasing approximate upper bound (DUB)" and validates its effectiveness through extensive experiments on public and real product datasets.