  In recent years, researchers attempt to utilize online social information to
alleviate data sparsity for collaborative filtering, based on the rationale
that social networks offers the insights to understand the behavioral patterns.
However, due to the overlook of inter-dependent knowledge across items (e.g.,
categories of products), existing social recommender systems are insufficient
to distill the heterogeneous collaborative signals from both user and item
sides. In this work, we propose a Self-Supervised Metagraph Infor-max Network
(SMIN) which investigates the potential of jointly incorporating social- and
knowledge-aware relational structures into the user preference representation
for recommendation. To model relation heterogeneity, we design a
metapath-guided heterogeneous graph neural network to aggregate feature
embeddings from different types of meta-relations across users and items,
em-powering SMIN to maintain dedicated representations for multi-faceted user-
and item-wise dependencies. Additionally, to inject high-order collaborative
signals, we generalize the mutual information learning paradigm under the
self-supervised graph-based collaborative filtering. This endows the expressive
modeling of user-item interactive patterns, by exploring global-level
collaborative relations and underlying isomorphic transformation property of
graph topology. Experimental results on several real-world datasets demonstrate
the effectiveness of our SMIN model over various state-of-the-art
recommendation methods. We release our source code at
https://github.com/SocialRecsys/SMIN.
