  Social recommender systems have drawn a lot of attention in many online web
services, because of the incorporation of social information between users in
improving recommendation results. Despite the significant progress made by
existing solutions, we argue that current methods fall short in two
limitations: (1) Existing social-aware recommendation models only consider
collaborative similarity between items, how to incorporate item-wise semantic
relatedness is less explored in current recommendation paradigms. (2) Current
social recommender systems neglect the entanglement of the latent factors over
heterogeneous relations (e.g., social connections, user-item interactions).
Learning the disentangled representations with relation heterogeneity poses
great challenge for social recommendation. In this work, we design a
Disentangled Graph Neural Network (DGNN) with the integration of latent memory
units, which empowers DGNN to maintain factorized representations for
heterogeneous types of user and item connections. Additionally, we devise new
memory-augmented message propagation and aggregation schemes under the graph
neural architecture, allowing us to recursively distill semantic relatedness
into the representations of users and items in a fully automatic manner.
Extensive experiments on three benchmark datasets verify the effectiveness of
our model by achieving great improvement over state-of-the-art recommendation
techniques. The source code is publicly available at:
https://github.com/HKUDS/DGNN.
