  Generating recommendations based on user-item interactions and user-user
social relations is a common use case in web-based systems. These connections
can be naturally represented as graph-structured data and thus utilizing graph
neural networks (GNNs) for social recommendation has become a promising
research direction. However, existing graph-based methods fails to consider the
bias offsets of users (items). For example, a low rating from a fastidious user
may not imply a negative attitude toward this item because the user tends to
assign low ratings in common cases. Such statistics should be considered into
the graph modeling procedure. While some past work considers the biases, we
argue that these proposed methods only treat them as scalars and can not
capture the complete bias information hidden in data. Besides, social
connections between users should also be differentiable so that users with
similar item preference would have more influence on each other. To this end,
we propose Graph-Based Decentralized Collaborative Filtering for Social
Recommendation (GDSRec). GDSRec treats the biases as vectors and fuses them
into the process of learning user and item representations. The statistical
bias offsets are captured by decentralized neighborhood aggregation while the
social connection strength is defined according to the preference similarity
and then incorporated into the model design. We conduct extensive experiments
on two benchmark datasets to verify the effectiveness of the proposed model.
Experimental results show that the proposed GDSRec achieves superior
performance compared with state-of-the-art related baselines. Our
implementations are available in \url{https://github.com/MEICRS/GDSRec}.
