  Recommender systems, which analyze users' preference patterns to suggest
potential targets, are indispensable in today's society. Collaborative
Filtering (CF) is the most popular recommendation model. Specifically, Graph
Neural Network (GNN) has become a new state-of-the-art for CF. In the GNN-based
recommender system, message dropout is usually used to alleviate the selection
bias in the user-item bipartite graph. However, message dropout might
deteriorate the recommender system's performance due to the randomness of
dropping out the outgoing messages based on the user-item bipartite graph. To
solve this problem, we propose a graph contrastive learning module for a
general recommender system that learns the embeddings in a self-supervised
manner and reduces the randomness of message dropout. Besides, many recommender
systems optimize models with pairwise ranking objectives, such as the Bayesian
Pairwise Ranking (BPR) based on a negative sampling strategy. However, BPR has
the following problems: suboptimal sampling and sample bias. We introduce a new
debiased contrastive loss to solve these problems, which provides sufficient
negative samples and applies a bias correction probability to alleviate the
sample bias. We integrate the proposed framework, including graph contrastive
module and debiased contrastive module with several Matrix Factorization(MF)
and GNN-based recommendation models. Experimental results on three public
benchmarks demonstrate the effectiveness of our framework.
