  Collaborative filtering (CF), as a standard method for recommendation with
implicit feedback, tackles a semi-supervised learning problem where most
interaction data are unobserved. Such a nature makes existing approaches highly
rely on mining negatives for providing correct training signals. However,
mining proper negatives is not a free lunch, encountering with a tricky
trade-off between mining informative hard negatives and avoiding false ones. We
devise a new approach named as Hardness-Aware Debiased Contrastive
Collaborative Filtering (HDCCF) to resolve the dilemma. It could sufficiently
explore hard negatives from two-fold aspects: 1) adaptively sharpening the
gradients of harder instances through a set-wise objective, and 2) implicitly
leveraging item/user frequency information with a new sampling strategy. To
circumvent false negatives, we develop a principled approach to improve the
reliability of negative instances and prove that the objective is an unbiased
estimation of sampling from the true negative distribution. Extensive
experiments demonstrate the superiority of the proposed model over existing CF
models and hard negative mining methods.
