Epsilon non-Greedy: A Bandit Approach for Unbiased Recommendation via
  Uniform Data
S. M. F. Sani Seyed Abbas Hosseini Hamid R. Rabiee
Often, recommendation systems employ continuous training, leading to a
self-feedback loop bias in which the system becomes biased toward its previous
recommendations. Recent studies have attempted to mitigate this bias by
collecting small amounts of unbiased data. While these studies have
successfully developed less biased models, they ignore the crucial fact that
the recommendations generated by the model serve as the training data for
subsequent training sessions. To address this issue, we propose a framework
that learns an unbiased estimator using a small amount of uniformly collected
data and focuses on generating improved training data for subsequent training
iterations. To accomplish this, we view recommendation as a contextual
multi-arm bandit problem and emphasize on exploring items that the model has a
limited understanding of. We introduce a new offline sequential training schema
that simulates real-world continuous training scenarios in recommendation
systems, offering a more appropriate framework for studying self-feedback bias.
We demonstrate the superiority of our model over state-of-the-art debiasing
methods by conducting extensive experiments using the proposed training schema.