Epsilon non-Greedy: A Bandit Approach for Unbiased Recommendation via Uniform Data
This addresses bias in recommendation systems for users and platforms, but it is incremental as it builds on existing debiasing methods with a new training schema.
The paper tackles the self-feedback loop bias in continuously trained recommendation systems by proposing a framework that uses a small amount of uniformly collected data to learn an unbiased estimator and generate improved training data for subsequent iterations, demonstrating superiority over state-of-the-art debiasing methods in experiments.
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.