  Social recommendation is effective in improving the recommendation
performance by leveraging social relations from online social networking
platforms. Social relations among users provide friends' information for
modeling users' interest in candidate items and help items expose to potential
consumers (i.e., item attraction). However, there are two issues haven't been
well-studied: Firstly, for the user interests, existing methods typically
aggregate friends' information contextualized on the candidate item only, and
this shallow context-aware aggregation makes them suffer from the limited
friends' information. Secondly, for the item attraction, if the item's past
consumers are the friends of or have a similar consumption habit to the
targeted user, the item may be more attractive to the targeted user, but most
existing methods neglect the relation enhanced context-aware item attraction.
To address the above issues, we proposed DICER (Dual Side Deep Context-aware
Modulation for SocialRecommendation). Specifically, we first proposed a novel
graph neural network to model the social relation and collaborative relation,
and on top of high-order relations, a dual side deep context-aware modulation
is introduced to capture the friends' information and item attraction.
Empirical results on two real-world datasets show the effectiveness of the
proposed model and further experiments are conducted to help understand how the
dual context-aware modulation works.
