  Session-based recommendation targets next-item prediction by exploiting user
behaviors within a short time period. Compared with other recommendation
paradigms, session-based recommendation suffers more from the problem of data
sparsity due to the very limited short-term interactions. Self-supervised
learning, which can discover ground-truth samples from the raw data, holds vast
potentials to tackle this problem. However, existing self-supervised
recommendation models mainly rely on item/segment dropout to augment data,
which are not fit for session-based recommendation because the dropout leads to
sparser data, creating unserviceable self-supervision signals. In this paper,
for informative session-based data augmentation, we combine self-supervised
learning with co-training, and then develop a framework to enhance
session-based recommendation. Technically, we first exploit the session-based
graph to augment two views that exhibit the internal and external
connectivities of sessions, and then we build two distinct graph encoders over
the two views, which recursively leverage the different connectivity
information to generate ground-truth samples to supervise each other by
contrastive learning. In contrast to the dropout strategy, the proposed
self-supervised graph co-training preserves the complete session information
and fulfills genuine data augmentation. Extensive experiments on multiple
benchmark datasets show that, session-based recommendation can be remarkably
enhanced under the regime of self-supervised graph co-training, achieving the
state-of-the-art performance.
