  Social recommendation task aims to predict users' preferences over items with
the incorporation of social connections among users, so as to alleviate the
sparse issue of collaborative filtering. While many recent efforts show the
effectiveness of neural network-based social recommender systems, several
important challenges have not been well addressed yet: (i) The majority of
models only consider users' social connections, while ignoring the
inter-dependent knowledge across items; (ii) Most of existing solutions are
designed for singular type of user-item interactions, making them infeasible to
capture the interaction heterogeneity; (iii) The dynamic nature of user-item
interactions has been less explored in many social-aware recommendation
techniques. To tackle the above challenges, this work proposes a
Knowledge-aware Coupled Graph Neural Network (KCGN) that jointly injects the
inter-dependent knowledge across items and users into the recommendation
framework. KCGN enables the high-order user- and item-wise relation encoding by
exploiting the mutual information for global graph structure awareness.
Additionally, we further augment KCGN with the capability of capturing dynamic
multi-typed user-item interactive patterns. Experimental studies on real-world
datasets show the effectiveness of our method against many strong baselines in
a variety of settings. Source codes are available at:
https://github.com/xhcdream/KCGN.
