  Social recommendations utilize social relations to enhance the representation
learning for recommendations. Most social recommendation models unify user
representations for the user-item interactions (collaborative domain) and
social relations (social domain). However, such an approach may fail to model
the users heterogeneous behavior patterns in two domains, impairing the
expressiveness of user representations. In this work, to address such
limitation, we propose a novel Disentangled contrastive learning framework for
social Recommendations DcRec. More specifically, we propose to learn
disentangled users representations from the item and social domains. Moreover,
disentangled contrastive learning is designed to perform knowledge transfer
between disentangled users representations for social recommendations.
Comprehensive experiments on various real-world datasets demonstrate the
superiority of our proposed model.
