  Negative sampling approaches are prevalent in implicit collaborative
filtering for obtaining negative labels from massive unlabeled data. As two
major concerns in negative sampling, efficiency and effectiveness are still not
fully achieved by recent works that use complicate structures and overlook risk
of false negative instances. In this paper, we first provide a novel
understanding of negative instances by empirically observing that only a few
instances are potentially important for model learning, and false negatives
tend to have stable predictions over many training iterations. Above findings
motivate us to simplify the model by sampling from designed memory that only
stores a few important candidates and, more importantly, tackle the untouched
false negative problem by favouring high-variance samples stored in memory,
which achieves efficient sampling of true negatives with high-quality.
Empirical results on two synthetic datasets and three real-world datasets
demonstrate both robustness and superiorities of our negative sampling method.
