  Social-aware recommendation approaches have been recognized as an effective
way to solve the data sparsity issue of traditional recommender systems. The
assumption behind is that the knowledge in social user-user connections can be
shared and transferred to the domain of user-item interactions, whereby to help
learn user preferences. However, most existing approaches merely adopt the
first-order connections among users during transfer learning, ignoring those
connections in higher orders. We argue that better recommendation performance
can also benefit from high-order social relations. In this paper, we propose a
novel Propagation-aware Transfer Learning Network (PTLN) based on the
propagation of social relations. We aim to better mine the sharing knowledge
hidden in social networks and thus further improve recommendation performance.
Specifically, we explore social influence in two aspects: (a) higher-order
friends have been taken into consideration by order bias; (b) different friends
in the same order will have distinct importance for recommendation by an
attention mechanism. Besides, we design a novel regularization to bridge the
gap between social relations and user-item interactions. We conduct extensive
experiments on two real-world datasets and beat other counterparts in terms of
ranking accuracy, especially for the cold-start users with few historical
interactions.
