  In implicit collaborative filtering (CF) task of recommender systems, recent
works mainly focus on model structure design with promising techniques like
graph neural networks (GNNs). Effective and efficient negative sampling methods
that suit these models, however, remain underdeveloped. One challenge is that
existing hard negative samplers tend to suffer from severer over-fitting in
model training. In this work, we first study the reason behind the
over-fitting, and illustrate it with the incorrect selection of false negative
instances with the support of experiments. In addition, we empirically observe
a counter-intuitive phenomenon, that is, polluting hard negative samples'
embeddings with a quite large proportional of positive samples' embeddings will
lead to remarkable performance gains for prediction accuracy. On top of this
finding, we present a novel negative sampling strategy, i.e.,
positive-dominated negative synthesizing (PDNS). Moreover, we provide
theoretical analysis and derive a simple equivalent algorithm of PDNS, where
only a soft factor is added in the loss function. Comprehensive experiments on
three real-world datasets demonstrate the superiority of our proposed method in
terms of both effectiveness and robustness.
