Jingtao Ding, Yuhan Quan, Quanming Yao, Yong Li, Depeng Jin
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. The implementation is available at https://github.com/dingjingtao/SRNS.