  Self-supervised learning (SSL), which can automatically generate ground-truth
samples from raw data, holds vast potential to improve recommender systems.
Most existing SSL-based methods perturb the raw data graph with uniform
node/edge dropout to generate new data views and then conduct the
self-discrimination based contrastive learning over different views to learn
generalizable representations. Under this scheme, only a bijective mapping is
built between nodes in two different views, which means that the
self-supervision signals from other nodes are being neglected. Due to the
widely observed homophily in recommender systems, we argue that the supervisory
signals from other nodes are also highly likely to benefit the representation
learning for recommendation. To capture these signals, a general socially-aware
SSL framework that integrates tri-training is proposed in this paper.
Technically, our framework first augments the user data views with the user
social information. And then under the regime of tri-training for multi-view
encoding, the framework builds three graph encoders (one for recommendation)
upon the augmented views and iteratively improves each encoder with
self-supervision signals from other users, generated by the other two encoders.
Since the tri-training operates on the augmented views of the same data sources
for self-supervision signals, we name it self-supervised tri-training.
Extensive experiments on multiple real-world datasets consistently validate the
effectiveness of the self-supervised tri-training framework for improving
recommendation. The code is released at https://github.com/Coder-Yu/QRec.
