  Binary pointwise labels (aka implicit feedback) are heavily leveraged by deep
learning based recommendation algorithms nowadays. In this paper we discuss the
limited expressiveness of these labels may fail to accommodate varying degrees
of user preference, and thus lead to conflicts during model training, which we
call annotation bias. To solve this issue, we find the soft-labeling property
of pairwise labels could be utilized to alleviate the bias of pointwise labels.
To this end, we propose a momentum contrast framework (MP2) that combines
pointwise and pairwise learning for recommendation. MP2 has a three-tower
network structure: one user network and two item networks. The two item
networks are used for computing pointwise and pairwise loss respectively. To
alleviate the influence of the annotation bias, we perform a momentum update to
ensure a consistent item representation. Extensive experiments on real-world
datasets demonstrate the superiority of our method against state-of-the-art
recommendation algorithms.
