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Sampling strategies have been widely applied in many recommendation systems to accelerate model learning from implicit feedback data. A typical strategy is to draw negative instances with uniform distribution, which however will severely affect model's convergency, stability, and even recommendation accuracy. A promising solution for this problem is to over-sample the "difficult" (a.k.a informative) instances that contribute more on training. But this will increase the risk of biasing the modelarXiv:2011.07739v1 fatcat:wktdowkk7be2dnou63g2pzoslq