Abstract
Recent years have witnessed rapid developments
on social recommendation techniques for improving the performance of recommender systems due
to the growing influence of social networks to our
daily life. The majority of existing social recommendation methods unify user representation for
the user-item interactions (item domain) and useruser connections (social domain). However, it may
restrain user representation learning in each respective domain, since users behave and interact differently in two domains, which makes their representations to be heterogeneous. In addition, most
of traditional recommender systems can not effi-
ciently optimize these objectives, since they utilize negative sampling technique which is unable to
provide enough informative guidance towards the
training during the optimization process. In this paper, to address the aforementioned challenges, we
propose a novel Deep Adversarial SOcial recommendation DASO. It adopts a bidirectional mapping method to transfer users’ information between
social domain and item domain using adversarial
learning. Comprehensive experiments on two realworld datasets show the effectiveness of the proposed method