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Often, recommendation systems employ continuous training, leading to aself-feedback loop bias in which the system becomes biased toward its previousrecommendations. Recent studies have attempted to mitigate this bias bycollecting small amounts of unbiased data. While these studies havesuccessfully developed less biased models, they ignore the crucial fact thatthe recommendations generated by the model serve as the training data forsubsequent training sessions. To address this issue, we propose a frameworkthat learns an unbiased estimator using a small amount of uniformly collecteddata and focuses on generating improved training data for subsequent trainingiterations. To accomplish this, we view recommendation as a contextualmulti-arm bandit problem and emphasize on exploring items that the model has alimited understanding of. We introduce a new offline sequential training schemathat simulates real-world continuous training scenarios in recommendationsystems, offering a more appropriate framework for studying self-feedback bias.We demonstrate the superiority of our model over state-of-the-art debiasingmethods by conducting extensive experiments using the proposed training schema.

Epsilon non-Greedy: A Bandit Approach for Unbiased Recommendation via Uniform Data