  Social connections play a vital role in improving the performance of
recommendation systems (RS). However, incorporating social information into RS
is challenging. Most existing models usually consider social influences in a
given session, ignoring that both users preferences and their friends
influences are evolving. Moreover, in real world, social relations are sparse.
Modeling dynamic influences and alleviating data sparsity is of great
importance. In this paper, we propose a unified framework named Dynamic
RElation Aware Model (DREAM) for social recommendation, which tries to model
both users dynamic interests and their friends temporal influences.
Specifically, we design temporal information encoding modules, because of which
user representations are updated in each session. The updated user
representations are transferred to relational-GAT modules, subsequently
influence the operations on social networks. In each session, to solve social
relation sparsity, we utilize glove-based method to complete social network
with virtual friends. Then we employ relational-GAT module over completed
social networks to update users representations. In the extensive experiments
on the public datasets, DREAM significantly outperforms the state-of-the-art
solutions.
