  Modern recommender systems often deal with a variety of user interactions,
e.g., click, forward, purchase, etc., which requires the underlying recommender
engines to fully understand and leverage multi-behavior data from users.
Despite recent efforts towards making use of heterogeneous data, multi-behavior
recommendation still faces great challenges. Firstly, sparse target signals and
noisy auxiliary interactions remain an issue. Secondly, existing methods
utilizing self-supervised learning (SSL) to tackle the data sparsity neglect
the serious optimization imbalance between the SSL task and the target task.
Hence, we propose a Multi-Behavior Self-Supervised Learning (MBSSL) framework
together with an adaptive optimization method. Specifically, we devise a
behavior-aware graph neural network incorporating the self-attention mechanism
to capture behavior multiplicity and dependencies. To increase the robustness
to data sparsity under the target behavior and noisy interactions from
auxiliary behaviors, we propose a novel self-supervised learning paradigm to
conduct node self-discrimination at both inter-behavior and intra-behavior
levels. In addition, we develop a customized optimization strategy through
hybrid manipulation on gradients to adaptively balance the self-supervised
learning task and the main supervised recommendation task. Extensive
experiments on five real-world datasets demonstrate the consistent improvements
obtained by MBSSL over ten state-of-the art (SOTA) baselines. We release our
model implementation at: https://github.com/Scofield666/MBSSL.git.
